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106 results on '"Equipe de Recherche en Ingénierie des Connaissances (ERIC)"'

1. Optimonotone measures for optimal rule discovery

Author: Le Bras, Yannick, Lenca, Philippe, Lallich, Stéphane, Département Logique des Usages, Sciences sociales et Sciences de l'Information ( LUSSI ), Université européenne de Bretagne ( UEB ) -Télécom Bretagne-Institut Mines-Télécom [Paris], Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance ( Lab-STICC ), École Nationale d'Ingénieurs de Brest ( ENIB ) -Université de Bretagne Sud ( UBS ) -Université de Brest ( UBO ) -Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques ( IBNM ), Université de Brest ( UBO ) -Université européenne de Bretagne ( UEB ) -ENSTA Bretagne-Institut Mines-Télécom [Paris]-Centre National de la Recherche Scientifique ( CNRS ), Lab-STICC_TB_CID_DECIDE, Université de Brest ( UBO ) -Université européenne de Bretagne ( UEB ) -ENSTA Bretagne-Institut Mines-Télécom [Paris]-Centre National de la Recherche Scientifique ( CNRS ) -École Nationale d'Ingénieurs de Brest ( ENIB ) -Université de Bretagne Sud ( UBS ) -Université de Brest ( UBO ) -Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques ( IBNM ), Equipe de Recherche en Ingénierie des Connaissances ( ERIC ), Université Lumière - Lyon 2 ( UL2 ), Département Logique des Usages, Sciences sociales et Sciences de l'Information (LUSSI), Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT), Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Equipe de Recherche en Ingénierie des Connaissances (ERIC), and Université Lumière - Lyon 2 (UL2)
Subjects: [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], [ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Optimal rules, Interestingness measure, Association rules, [ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI], [ STAT.ML ] Statistics [stat]/Machine Learning [stat.ML], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract: International audience; Many studies have shown the limits of the support/confidence framework used in APRIORI -like algorithms to mine association rules. There are a lot of efficient implementations based on the antimonotony property of the support, but candidate set generation (e.g., frequent item set mining) is still costly. In addition, many rules are uninteresting or redundant and one can miss interesting rules like nuggets. We are thus facing a complexity issue and a quality issue. One solution is to not use frequent itemset mining and to focus as soon as possible on interesting rules using additional interestingness measures. We present here a formal framework that allows us to make a link between analytic and algorithmic properties of interestingness measures. We introduce the notion of optimonotony in relation with the optimal rule discovery framework. We then demonstrate a necessary and sufficient condition for the existence of optimonotony. This result can thus be applied to classify the measures. We study the case of 39 classical measures and show that 31 of them are optimonotone. These optimonotone measures can thus be used with an underlying pruning strategy. Empirical evaluations show that the pruning strategy is efficient and leads tothe discovery of nuggets using an optimonotone measure and without the support constraint.
Published: 2012
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2. Formal framework for the study of algorithmic properties of objective interestingness measures

Author: Le Bras , Yannick, Lenca , Philippe, Lallich , Stéphane, Département Logique des Usages, Sciences sociales et Sciences de l'Information ( LUSSI ), Université européenne de Bretagne ( UEB ) -Télécom Bretagne-Institut Mines-Télécom [Paris], Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance ( Lab-STICC ), École Nationale d'Ingénieurs de Brest ( ENIB ) -Université de Bretagne Sud ( UBS ) -Université de Brest ( UBO ) -Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques ( IBNM ), Université de Brest ( UBO ) -Université européenne de Bretagne ( UEB ) -ENSTA Bretagne-Institut Mines-Télécom [Paris]-Centre National de la Recherche Scientifique ( CNRS ), Lab-STICC_TB_CID_DECIDE, Université de Brest ( UBO ) -Université européenne de Bretagne ( UEB ) -ENSTA Bretagne-Institut Mines-Télécom [Paris]-Centre National de la Recherche Scientifique ( CNRS ) -École Nationale d'Ingénieurs de Brest ( ENIB ) -Université de Bretagne Sud ( UBS ) -Université de Brest ( UBO ) -Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques ( IBNM ), Equipe de Recherche en Ingénierie des Connaissances ( ERIC ), Université Lumière - Lyon 2 ( UL2 ), Département Logique des Usages, Sciences sociales et Sciences de l'Information (LUSSI), Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT), Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2), and Télécom Bretagne (devenu IMT Atlantique), Ex-Bibliothèque
Subjects: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], [ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB], Interestingness measure, Association rules, [ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI], Data mining, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], [ STAT.ML ] Statistics [stat]/Machine Learning [stat.ML], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract: International audience; Association Rules Discovery is an increasing subdomain of Datamining. Many works have focused on the extraction and the evaluation of the association rules, leading to many technical improvments on the algorithms, and many different measures. But few number of them have tried to merge the both. We introduce here a formal framework for the study of association rules and interestingness measures that allows an analytic study of these objects. This framework is based on the contingency table of a rule and let us make a link between analytic properties of the measures and algorithmic properties. We give as example the case of three algorithmic properties for the extraction of association rules that were generalized and applied with the help of this framework. These properties allow a pruning of the search space based on a large number of measures and without any support constraint.
Published: 2012

3. Mining classification rules without support: an anti-monotone property of jaccard measure

Author: Philippe Lenca, Stéphane Lallich, Yannick Le Bras, Département Logique des Usages, Sciences sociales et Sciences de l'Information ( LUSSI ), Université européenne de Bretagne ( UEB ) -Télécom Bretagne-Institut Mines-Télécom [Paris], Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (UMR 3192) ( Lab-STICC ), Université européenne de Bretagne ( UEB ) -Université de Bretagne Sud ( UBS ) -Université de Brest ( UBO ) -Institut Brestois du Numérique et des Mathématiques ( IBNM ), Université de Brest ( UBO ) -Télécom Bretagne-Institut Mines-Télécom [Paris]-Centre National de la Recherche Scientifique ( CNRS ), Lab-STICC_TB_CID_DECIDE, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance ( Lab-STICC ), École Nationale d'Ingénieurs de Brest ( ENIB ) -Université de Bretagne Sud ( UBS ) -Université de Brest ( UBO ) -Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques ( IBNM ), Université de Brest ( UBO ) -Université européenne de Bretagne ( UEB ) -ENSTA Bretagne-Institut Mines-Télécom [Paris]-Centre National de la Recherche Scientifique ( CNRS ) -École Nationale d'Ingénieurs de Brest ( ENIB ) -Université de Bretagne Sud ( UBS ) -Université de Brest ( UBO ) -Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques ( IBNM ), Université de Brest ( UBO ) -Université européenne de Bretagne ( UEB ) -ENSTA Bretagne-Institut Mines-Télécom [Paris]-Centre National de la Recherche Scientifique ( CNRS ), Equipe de Recherche en Ingénierie des Connaissances ( ERIC ), Université Lumière - Lyon 2 ( UL2 ), Département Logique des Usages, Sciences sociales et Sciences de l'Information (LUSSI), Institut Mines-Télécom [Paris] (IMT)-Télécom Bretagne-Université européenne de Bretagne - European University of Brittany (UEB), Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (UMR 3192) (Lab-STICC), Université européenne de Bretagne - European University of Brittany (UEB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Equipe de Recherche en Ingénierie des Connaissances (ERIC), and Université Lumière - Lyon 2 (UL2)
Subjects: Property (philosophy), Jaccard index, ComputerApplications_COMPUTERSINOTHERSYSTEMS, 02 engineering and technology, Space (commercial competition), Anti-monotony property, computer.software_genre, Classification rules, Measure (mathematics), [ INFO.INFO-LG ] Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Pruning (decision trees), [ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI], [ STAT.ML ] Statistics [stat]/Machine Learning [stat.ML], Mathematics, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], 16. Peace & justice, Constraint (information theory), Monotone polygon, ComputingMethodologies_PATTERNRECOGNITION, [ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB], 020201 artificial intelligence & image processing, Data mining, computer, Jaccard measure
Abstract: International audience; We propose a general definition of anti-monotony, and study the anti-monotone property of the Jaccard measure for classification rules. The discovered property can be inserted in an Apriori-like algorithm and can prune the search space without any support constraint. Moreover, the algorithm is complete since, it outputs all interesting rules with respect to the measure of Jaccard. The proposed pruning strategy can then be used to efficiently find nuggets of knowledge.
Published: 2011
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4. A robustness measure of association rules

Author: Stéphane Lallich, Yannick Le Bras, Patrick Meyer, Philippe Lenca, Département Logique des Usages, Sciences sociales et Sciences de l'Information ( LUSSI ), Université européenne de Bretagne ( UEB ) -Télécom Bretagne-Institut Mines-Télécom [Paris], Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (UMR 3192) ( Lab-STICC ), Université européenne de Bretagne ( UEB ) -Université de Bretagne Sud ( UBS ) -Université de Brest ( UBO ) -Institut Brestois du Numérique et des Mathématiques ( IBNM ), Université de Brest ( UBO ) -Télécom Bretagne-Institut Mines-Télécom [Paris]-Centre National de la Recherche Scientifique ( CNRS ), Lab-STICC_TB_CID_DECIDE, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance ( Lab-STICC ), École Nationale d'Ingénieurs de Brest ( ENIB ) -Université de Bretagne Sud ( UBS ) -Université de Brest ( UBO ) -Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques ( IBNM ), Université de Brest ( UBO ) -Université européenne de Bretagne ( UEB ) -ENSTA Bretagne-Institut Mines-Télécom [Paris]-Centre National de la Recherche Scientifique ( CNRS ) -École Nationale d'Ingénieurs de Brest ( ENIB ) -Université de Bretagne Sud ( UBS ) -Université de Brest ( UBO ) -Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques ( IBNM ), Université de Brest ( UBO ) -Université européenne de Bretagne ( UEB ) -ENSTA Bretagne-Institut Mines-Télécom [Paris]-Centre National de la Recherche Scientifique ( CNRS ), Equipe de Recherche en Ingénierie des Connaissances ( ERIC ), Université Lumière - Lyon 2 ( UL2 ), Département Logique des Usages, Sciences sociales et Sciences de l'Information (LUSSI), Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT), Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (UMR 3192) (Lab-STICC), Université européenne de Bretagne - European University of Brittany (UEB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Equipe de Recherche en Ingénierie des Connaissances (ERIC), and Université Lumière - Lyon 2 (UL2)
Subjects: Association rule learning, 02 engineering and technology, Knowledge discovery in databases, computer.software_genre, Machine learning, Association rules, 01 natural sciences, 010104 statistics & probability, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Robustness (computer science), 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Statistical analysis, 0101 mathematics, Formal description, [ STAT.ML ] Statistics [stat]/Machine Learning [stat.ML], Mathematics, [ INFO.INFO-RO ] Computer Science [cs]/Operations Research [cs.RO], [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], business.industry, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], 16. Peace & justice, [ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB], Robustness measure, Data mining, Artificial intelligence, business, computer
Abstract: International audience; We propose a formal definition of the robustness of association rules for interestingness measures. It is a central concept in the evaluation of the rules and has only been studied unsatisfactorily up to now. It is crucial because a good rule (according to a given quality measure) might turn out as a very fragile rule with respect to small variations in the data. The robustness measure that we propose here is based on a model we proposed in a previous work. It depends on the selected quality measure, the value taken by the rule and the minimal acceptance threshold chosen by the user. We present a few properties of this robustness, detail its use in practice and show the outcomes of various experiments. Furthermore, we compare our results to classical tools of statistical analysis of association rules. All in all, we present a new perspective on the evaluation of association rules.
Published: 2010
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5. Mining interesting rules without support requirement: a general universal existential upward closure property

Author: Le Bras, Yannick, Lenca, Philippe, Lallich, Stéphane, Département Logique des Usages, Sciences sociales et Sciences de l'Information ( LUSSI ), Université européenne de Bretagne ( UEB ) -Télécom Bretagne-Institut Mines-Télécom [Paris], Lab-STICC_TB_CID_DECIDE, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance ( Lab-STICC ), École Nationale d'Ingénieurs de Brest ( ENIB ) -Université de Bretagne Sud ( UBS ) -Université de Brest ( UBO ) -Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques ( IBNM ), Université de Brest ( UBO ) -Université européenne de Bretagne ( UEB ) -ENSTA Bretagne-Institut Mines-Télécom [Paris]-Centre National de la Recherche Scientifique ( CNRS ) -École Nationale d'Ingénieurs de Brest ( ENIB ) -Université de Bretagne Sud ( UBS ) -Université de Brest ( UBO ) -Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques ( IBNM ), Université de Brest ( UBO ) -Université européenne de Bretagne ( UEB ) -ENSTA Bretagne-Institut Mines-Télécom [Paris]-Centre National de la Recherche Scientifique ( CNRS ), Equipe de Recherche en Ingénierie des Connaissances ( ERIC ), Université Lumière - Lyon 2 ( UL2 ), Département Logique des Usages, Sciences sociales et Sciences de l'Information (LUSSI), Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT), Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Equipe de Recherche en Ingénierie des Connaissances (ERIC), and Université Lumière - Lyon 2 (UL2)
Subjects: [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], [ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Pruning strategy, General universal existential upward closure, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], Support/confidence framework, Rule mining, [ INFO.INFO-DS ] Computer Science [cs]/Data Structures and Algorithms [cs.DS], [ STAT.ML ] Statistics [stat]/Machine Learning [stat.ML], Interesting rules
Abstract: International audience; Many studies have shown the limits of support/confidence framework used in Apriori-like algorithms to mine association rules. There are a lot of efficient implementations based on the antimonotony property of the support. But candidate set generation is still costly and many rules are uninteresting or redundant. In addition one can miss interesting rules like nuggets. We are thus facing a complexity issue and a quality issue. One solution is to get rid of frequent itemset mining and to focus as soon as possible on interesting rules. For that purpose algorithmic properties were first studied, especially for the confidence. They allow to find all confident rules without a preliminary support pruning. Recently, in the case of class association rules, the Universal Existential Upward Closure property of confidence has been exploited in an efficient manner. Indeed, it allows to use a pruning strategy for an Apriori-like but top down associative classification rules algorithm. We present a new formal framework which allows us to make the link between analytic and algorithmic properties of the measures. We then apply this framework to propose a General Universal Existential Upward Closure. We demonstrate a necessary and a sufficient condition of existence for this property. These results are then applied to 32 measures and we show that 13 of them do have the GUEUC property.
Published: 2010
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6. Construction of an off-centered entropy for the supervised learning of imbalanced classes: some first results

Author: Stéphane Lallich, Benoît Vaillant, Philippe Lenca, Lab-STICC_TB_CID_DECIDE, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance ( Lab-STICC ), École Nationale d'Ingénieurs de Brest ( ENIB ) -Université de Bretagne Sud ( UBS ) -Université de Brest ( UBO ) -Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques ( IBNM ), Université de Brest ( UBO ) -Université européenne de Bretagne ( UEB ) -ENSTA Bretagne-Institut Mines-Télécom [Paris]-Centre National de la Recherche Scientifique ( CNRS ) -École Nationale d'Ingénieurs de Brest ( ENIB ) -Université de Bretagne Sud ( UBS ) -Université de Brest ( UBO ) -Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques ( IBNM ), Université de Brest ( UBO ) -Université européenne de Bretagne ( UEB ) -ENSTA Bretagne-Institut Mines-Télécom [Paris]-Centre National de la Recherche Scientifique ( CNRS ), Département Logique des Usages, Sciences sociales et Sciences de l'Information ( LUSSI ), Université européenne de Bretagne ( UEB ) -Télécom Bretagne-Institut Mines-Télécom [Paris], Equipe de Recherche en Ingénierie des Connaissances ( ERIC ), Université Lumière - Lyon 2 ( UL2 ), Laboratoire de recherche en informatique et ses applications (Université de Bretagne Sud) ( VALORIA ), Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Département Logique des Usages, Sciences sociales et Sciences de l'Information (LUSSI), Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT), Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2), Laboratoire de recherche en informatique et ses applications (Université de Bretagne Sud) (VALORIA), and Télécom Bretagne (devenu IMT Atlantique), Ex-Bibliothèque
Subjects: Computer Science::Machine Learning, Statistics and Probability, Decision tree, Data_CODINGANDINFORMATIONTHEORY, 02 engineering and technology, Machine learning, computer.software_genre, 01 natural sciences, Joint entropy, Rényi entropy, 010104 statistics & probability, [INFO.INFO-CY]Computer Science [cs]/Computers and Society [cs.CY], Statistics, 0202 electrical engineering, electronic engineering, information engineering, Mathematics::Metric Geometry, Entropy (information theory), 0101 mathematics, ComputingMilieux_MISCELLANEOUS, Mathematics, business.industry, Principle of maximum entropy, Supervised learning, TheoryofComputation_GENERAL, [ INFO.INFO-CY ] Computer Science [cs]/Computers and Society [cs.CY], [INFO.INFO-CY] Computer Science [cs]/Computers and Society [cs.CY], Maximum entropy probability distribution, 020201 artificial intelligence & image processing, Transfer entropy, Artificial intelligence, business, computer
Abstract: In supervised learning, especially in decision tree induction, many measures are based on the concept of entropy. A major characteristic of entropies is that they take their maximal value when the ...
Published: 2010

7. Classifying very-high-dimensional data with random forests of oblique decision trees

Author: Do, Thanh Nghi, Lenca, Philippe, Lallich, Stéphane, Pham, Nguyen-Khang, Département Logique des Usages, Sciences sociales et Sciences de l'Information ( LUSSI ), Université européenne de Bretagne ( UEB ) -Télécom Bretagne-Institut Mines-Télécom [Paris], Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (UMR 3192) ( Lab-STICC ), Université européenne de Bretagne ( UEB ) -Université de Bretagne Sud ( UBS ) -Université de Brest ( UBO ) -Institut Brestois du Numérique et des Mathématiques ( IBNM ), Université de Brest ( UBO ) -Télécom Bretagne-Institut Mines-Télécom [Paris]-Centre National de la Recherche Scientifique ( CNRS ), College of Information and Communication Technology (.) ( CICT ), Lab-STICC_TB_CID_DECIDE, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance ( Lab-STICC ), École Nationale d'Ingénieurs de Brest ( ENIB ) -Université de Bretagne Sud ( UBS ) -Université de Brest ( UBO ) -Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques ( IBNM ), Université de Brest ( UBO ) -Université européenne de Bretagne ( UEB ) -ENSTA Bretagne-Institut Mines-Télécom [Paris]-Centre National de la Recherche Scientifique ( CNRS ) -École Nationale d'Ingénieurs de Brest ( ENIB ) -Université de Bretagne Sud ( UBS ) -Université de Brest ( UBO ) -Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques ( IBNM ), Université de Brest ( UBO ) -Université européenne de Bretagne ( UEB ) -ENSTA Bretagne-Institut Mines-Télécom [Paris]-Centre National de la Recherche Scientifique ( CNRS ), Equipe de Recherche en Ingénierie des Connaissances ( ERIC ), Université Lumière - Lyon 2 ( UL2 ), Institut de Recherche en Informatique et Systèmes Aléatoires ( IRISA ), CentraleSupélec-Université de Rennes 1 ( UR1 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -Télécom Bretagne-Institut National des Sciences Appliquées ( INSA ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -École normale supérieure - Rennes ( ENS Rennes ) -Centre National de la Recherche Scientifique ( CNRS ) -Université de Bretagne Sud ( UBS ), Département Logique des Usages, Sciences sociales et Sciences de l'Information (LUSSI), Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT), Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (UMR 3192) (Lab-STICC), Université européenne de Bretagne - European University of Brittany (UEB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), College of Information and Communication Technology (.) (CICT), Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Université européenne de Bretagne - European University of Brittany (UEB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), and Télécom Bretagne (devenu IMT Atlantique), Ex-Bibliothèque
Subjects: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Oblique decision trees, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], [ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB], [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], Random forests, [ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI], Very-high-dimensional data, [ INFO.INFO-LG ] Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract: International audience; The random forests method is one of the most successful ensemble methods. However, random forests do not have high performance when dealing with very-high-dimensional data in presence of dependencies. In this case one can expect that there exist many combinations between the variables and unfortunately the usual random forests method does not effectively exploit this situation. We here investigate a new approach for supervised classification with a huge number of numerical attributes. We propose a random oblique decision trees method. It consists of randomly choosing a subset of predictive attributes and it uses SVM as a split function of these attributes.We compare, on 25 datasets, the effectiveness with classical measures (e.g. precision, recall, F1-measure and accuracy) of random forests of random oblique decision trees with SVMs and random forests of C4.5. Our proposal has significant better performance on very-high-dimensional datasets with slightly better results on lower dimensional datasets.
Published: 2010

8. All-monotony: a generalization of the all-confidence antimonotony

Author: Sorin Moga, Yannick Le Bras, Philippe Lenca, Stéphane Lallich, Département Logique des Usages, Sciences sociales et Sciences de l'Information (LUSSI), Institut Mines-Télécom [Paris] (IMT)-Télécom Bretagne-Université européenne de Bretagne - European University of Brittany (UEB), Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (UMR 3192) (Lab-STICC), Université européenne de Bretagne - European University of Brittany (UEB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Lab-STICC_TB_CID_DECIDE, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2), Télécom Bretagne (devenu IMT Atlantique), Ex-Bibliothèque, Département Logique des Usages, Sciences sociales et Sciences de l'Information ( LUSSI ), Université européenne de Bretagne ( UEB ) -Télécom Bretagne-Institut Mines-Télécom [Paris], Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (UMR 3192) ( Lab-STICC ), Université européenne de Bretagne ( UEB ) -Université de Bretagne Sud ( UBS ) -Université de Brest ( UBO ) -Institut Brestois du Numérique et des Mathématiques ( IBNM ), Université de Brest ( UBO ) -Télécom Bretagne-Institut Mines-Télécom [Paris]-Centre National de la Recherche Scientifique ( CNRS ), Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance ( Lab-STICC ), École Nationale d'Ingénieurs de Brest ( ENIB ) -Université de Bretagne Sud ( UBS ) -Université de Brest ( UBO ) -Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques ( IBNM ), Université de Brest ( UBO ) -Université européenne de Bretagne ( UEB ) -ENSTA Bretagne-Institut Mines-Télécom [Paris]-Centre National de la Recherche Scientifique ( CNRS ) -École Nationale d'Ingénieurs de Brest ( ENIB ) -Université de Bretagne Sud ( UBS ) -Université de Brest ( UBO ) -Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques ( IBNM ), Université de Brest ( UBO ) -Université européenne de Bretagne ( UEB ) -ENSTA Bretagne-Institut Mines-Télécom [Paris]-Centre National de la Recherche Scientifique ( CNRS ), Equipe de Recherche en Ingénierie des Connaissances ( ERIC ), and Université Lumière - Lyon 2 ( UL2 )
Subjects: Apriori algorithm, Theoretical computer science, Property (philosophy), Association rule learning, Computer science, Generalization, Probability density function, 02 engineering and technology, Machine learning, computer.software_genre, Measure (mathematics), [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB], [ STAT.ML ] Statistics [stat]/Machine Learning [stat.ML], ComputingMilieux_MISCELLANEOUS, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], business.industry, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], [ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB], Transformation (function), 020201 artificial intelligence & image processing, Artificial intelligence, Focus (optics), business, computer
Abstract: Many studies have shown the limits of support/confidence framework used in Apriori-like algorithms to mine association rules. One solution to cope with this limitation is to get rid of frequent itemset mining and to focus as soon as possible on interesting rules. Many works have focussed on the algorithmic properties of the confidence. In particular, the all-confidence which is a transformation of the confidence, has the antimonotone property. In this paper, we generalize the all-confidence by associating to any measure its corresponding all-measure. We present a formal framework which allows us to make the link between analytic and algorithmic properties of the all-measure. We then propose the notion of all-monotony which corresponds to the monotony property of the all-measure. We show that there are 5 out of 37 measures which can be transformed into an antimonotone measure.
Published: 2009

9. Quality issues, measures of interestingness and evaluation of data mining models

Author: Lenca, Philippe, Lallich, Stéphane, Département Logique des Usages, Sciences sociales et Sciences de l'Information (LUSSI), Institut Mines-Télécom [Paris] (IMT)-Télécom Bretagne-Université européenne de Bretagne - European University of Brittany (UEB), Lab-STICC_TB_CID_DECIDE, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2), Philippe Lenca, Stéphane Lallich, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance ( Lab-STICC ), École Nationale d'Ingénieurs de Brest ( ENIB ) -Université de Bretagne Sud ( UBS ) -Université de Brest ( UBO ) -Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques ( IBNM ), Université de Brest ( UBO ) -Université européenne de Bretagne ( UEB ) -ENSTA Bretagne-Institut Mines-Télécom [Paris]-Centre National de la Recherche Scientifique ( CNRS ) -École Nationale d'Ingénieurs de Brest ( ENIB ) -Université de Bretagne Sud ( UBS ) -Université de Brest ( UBO ) -Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques ( IBNM ), Université de Brest ( UBO ) -Université européenne de Bretagne ( UEB ) -ENSTA Bretagne-Institut Mines-Télécom [Paris]-Centre National de la Recherche Scientifique ( CNRS ), Département Logique des Usages, Sciences sociales et Sciences de l'Information ( LUSSI ), Université européenne de Bretagne ( UEB ) -Télécom Bretagne-Institut Mines-Télécom [Paris], Equipe de Recherche en Ingénierie des Connaissances ( ERIC ), Université Lumière - Lyon 2 ( UL2 ), Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT), Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), and Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)
Subjects: [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], [ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI], ComputingMilieux_MISCELLANEOUS, [ STAT.ML ] Statistics [stat]/Machine Learning [stat.ML], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract: pkdd09ll; International audience
Published: 2009

10. Towards an adaptive curation services composition based on machine learning

Author: Khouloud Boukadi, Firas Zouari, Nadia Kabachi, Chirine Ghedira-Guegan, Service Oriented Computing (SOC), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), SID, Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2)-Université Lumière - Lyon 2 (UL2), Multimedia, InfoRmation systems and Advanced Computing Laboratory (MIRACL), Faculté des Sciences Economiques et de Gestion de Sfax (FSEG Sfax), Université de Sfax - University of Sfax-Université de Sfax - University of Sfax, Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Centre de Recherche Magellan, Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Université de Lyon-Institut d'Administration des Entreprises (IAE) - Lyon, Département Organisation et Modélisation des Systèmes Industriels (OMSI-ENSMSE), École des Mines de Saint-Étienne (Mines Saint-Étienne MSE), and Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Centre G2I
Subjects: Data curation, business.industry, Computer science, Process (engineering), Data management, Big data, Unstructured data, computer.software_genre, Machine learning, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Data visualization, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Reinforcement learning, Artificial intelligence, Web service, business, computer, ComputingMilieux_MISCELLANEOUS
Abstract: Data curation deals with managing the data by applying different tasks such as extraction, enrichment, cleaning to fit the purpose of use. Indeed, nowadays, there is an increasing need to implement such tasks in the big data era to maintain data management. Big data is involved in decision processes to perform analysis, visualization, prediction, etc. Thus, there is a dependency between the generated outcomes and the input data of such a process. Therefore, decision process features (e.g., decision context, user constraints, and requirements) need to be taken into account during the data management process, including the data curation phase. Although the proposed curation approaches in the literature are diverse, most of them are static and do not consider the decision process features. Moreover, most of the proposals are dedicated to curating a specific data source format (e.g., structured/unstructured data source). To overcome these limitations, we propose a new approach ACUSEC (Adaptive CUration SErvice Composition) that ensures adaptive curation services composition by considering different features: the source type, the user constraints and preferences, and the decision context. To do so, we rely on AI and machine learning mechanisms such as reinforcement learning. Following the approach's definition, we conducted experiments that show encouraging results in overall execution time and adaptation to the above features.
Published: 2021
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11. Learning rule sets and Sugeno integrals for monotonic classification problems

Author: Miguel Couceiro, Didier Dubois, Henri Prade, Agnès Rico, Quentin Brabant, Knowledge representation, reasonning (ORPAILLEUR), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Argumentation, Décision, Raisonnement, Incertitude et Apprentissage (IRIT-ADRIA), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Centre National de la Recherche Scientifique (CNRS), Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2), This work is supported by ANR-11-LABX-0040- CIMI (Centre International de Mathématiques et d’Informatique) within the program ANR-11-IDEX-0002-02, project ISIPA., ANR-11-IDEX-0002,UNITI,Université Fédérale de Toulouse(2011), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), Université Toulouse 1 Capitole (UT1)-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), and ANR-11-IDEX-0002-02/11-LABX-0040,CIMI,Centre International de Mathématiques et d’Informatique (de Toulouse)(2011)
Subjects: monotonicity constraint, 0209 industrial biotechnology, Theoretical computer science, Logic, Sugeno integral, Monotonic classification, 02 engineering and technology, 020901 industrial engineering & automation, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Artificial Intelligence, Learning rule, decomposable model, 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], Mathematics, Training set, decision rules, Decision rule, MCDA, 16. Peace & justice, Multiple-criteria decision analysis, Local utility, 020201 artificial intelligence & image processing, Classifier (UML)
Abstract: International audience; In some variants of the supervised classification setting, the domains of the attributes and the set of classes are totally ordered sets. The task of learning a classifier that is nondecreasing w.r.t. each attribute is called monotonic classification. Several kinds of models can be used in this task; in this paper , we focus on decision rules. We propose a method for learning a set of decision rules that optimally fits the training data while favoring short rules over long ones. We give new results on the representation of sets of if-then rules by extensions of Sugeno integrals to distinct attribute domains, where local utility functions are used to map attribute domains to a common totally ordered scale. We study whether such qualitative extensions of Sugeno integral provide compact representations of large sets of decision rules.
Published: 2020
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12. Adaptive Multi-agent-based Alert System for Diseases’ Detection

Author: Ibtihel Selmi, Sana Ben Abdallah, Chirine Ghedira Guegan, Hajer Baazaoui, Nadia Kabachi, Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2), Laboratoire de recherche en Génie Logiciel, Applications distribuées, Systèmes décisionnels et Imagerie intelligente [Manouba] (RIADI), École Nationale des Sciences de l'Informatique [Manouba] (ENSI), Université de la Manouba [Tunisie] (UMA)-Université de la Manouba [Tunisie] (UMA), Service Oriented Computing (SOC), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Université Lumière - Lyon 2 (UL2)
Subjects: medicine.medical_specialty, Health professionals, Computer science, Public health, 030232 urology & nephrology, Context (language use), 02 engineering and technology, Data science, 3. Good health, 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA], Epidemiology, 0202 electrical engineering, electronic engineering, information engineering, medicine, 020201 artificial intelligence & image processing, [INFO]Computer Science [cs], Alert system, ComputingMilieux_MISCELLANEOUS
Abstract: Monitoring the epidemiological situation of public health remains a challenge, given the exponential growth of health data. Indeed, these latter come from various sources such as, health professionals, biologists, experts in health organizations, etc. In this context, reconciling the search for the greatest amount of appropriate signals and the way of treating them, is the ultimate challenge.
Published: 2020
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13. Sugeno Integral for Rule-Based Ordinal Classification

Author: Quentin Brabant, Miguel Couceiro, Didier Dubois, Henri Prade, Agnès Rico, Knowledge representation, reasonning (ORPAILLEUR), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision (LAMSADE), Université Paris Dauphine-PSL, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Argumentation, Décision, Raisonnement, Incertitude et Apprentissage (IRIT-ADRIA), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), Centre National de la Recherche Scientifique (CNRS), Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2), International Joint Conference on Artificial Intelligence, Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), and Université Fédérale Toulouse Midi-Pyrénées
Subjects: [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-DM]Computer Science [cs]/Discrete Mathematics [cs.DM]
Abstract: International audience; We present a method for modeling empirical data by a rule set in ordinal classification problems. This method is nonparametric and uses an intermediary model based on Sugeno integral. The accuracy of rule sets thus obtained is competitive with other rule-based classifiers. Special attention is given to the length of rules, i.e., number of conditions.
Published: 2018

14. Impact of the Query Set on the Evaluation of Expert Finding Systems

Author: Brochier, R., Adrien Guille, Rothan, B., Velcin, J., Equipe de Recherche en Ingénierie des Connaissances (ERIC), and Université Lumière - Lyon 2 (UL2)
Subjects: FOS: Computer and information sciences, expert finding · recommender system · evaluation, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], Information Retrieval (cs.IR), Computer Science - Information Retrieval
Abstract: International audience; Expertise is a loosely defined concept that is hard to formalize. Much research has focused on designing efficient algorithms for expert finding in large databases in various application domains. The evaluation of such recommender systems lies most of the time on human-annotated sets of experts associated with topics. The protocol of evaluation consists in using the namings or short descriptions of these topics as raw queries in order to rank the available set of candidates. Several measures taken from the field of information retrieval are then applied to rate the rankings of candidates against the ground truth set of experts. In this paper, we apply this topic-query evaluation methodology with the AMiner data and explore a new document-query methodology to evaluate experts retrieval from a set of queries sampled directly from the experts documents. Specifically, we describe two datasets extracted from AMiner, three baseline algorithms from the literature based on several document representations and provide experiment results to show that using a wide range of more realistic queries provides different evaluation results to the usual topic-queries.
Published: 2018

15. Fuzzy Extensions of Conceptual Structures of Comparison

Author: Henri Prade, Agnès Rico, Didier Dubois, Argumentation, Décision, Raisonnement, Incertitude et Apprentissage (IRIT-ADRIA), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Centre National de la Recherche Scientifique (CNRS), Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2), Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - INPT (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), Université Claude Bernard-Lyon I - UCBL (FRANCE), Université Lumière-Lyon 2 (FRANCE), and Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Subjects: Theoretical computer science, Square of opposition, Inequality, Computer science, media_common.quotation_subject, Analogy, 02 engineering and technology, [INFO.INFO-DM]Computer Science [cs]/Discrete Mathematics [cs.DM], 01 natural sciences, Fuzzy logic, 0202 electrical engineering, electronic engineering, information engineering, Mathématique discrète, Logical hexagon, 0101 mathematics, Difference Similarity, ComputingMilieux_MISCELLANEOUS, media_common, Hexagon of opposition, Ordered weighted min, Modalities, 010102 general mathematics, Syllogism, 16. Peace & justice, 020201 artificial intelligence & image processing
Abstract: Comparing two items (objects, images) involves a set of relevant attributes whose values are compared. Such a comparison may be expressed in terms of different modalities such as identity, similarity, difference, opposition, analogy. Recently J.-Y. Beziau has proposed an “analogical hexagon” that organizes the relations linking these modalities. The hexagon structure extends the logical square of opposition invented in Aristotle time (in relation with the theory of syllogisms). The interest of these structures has been recently advocated in logic and in artificial intelligence. When non-Boolean attributes are involved, elementary comparisons may be a matter of degree. Moreover, attributes may not have the same importance. One might only consider most attributes rather than all of them, using operators such as ordered weighted min and max. The paper studies in which ways the logical hexagon structure may be preserved in such gradual extensions. As an illustration, we start with the hexagon of equality and inequality due to Blanche and extend it with fuzzy equality and fuzzy inequality.
Published: 2018

16. Classifying many-class high-dimensional fingerprint datasets using random forest of oblique decision trees

Author: Thanh-Nghi Do, Philippe Lenca, Stéphane Lallich, College of Information Technology ( CIT ), Can Tho University [Vietnam] ( CTU ), Département Logique des Usages, Sciences sociales et Sciences de l'Information ( LUSSI ), Université européenne de Bretagne ( UEB ) -Télécom Bretagne-Institut Mines-Télécom [Paris], Lab-STICC_TB_CID_DECIDE, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance ( Lab-STICC ), École Nationale d'Ingénieurs de Brest ( ENIB ) -Université de Bretagne Sud ( UBS ) -Université de Brest ( UBO ) -Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques ( IBNM ), Université de Brest ( UBO ) -Université européenne de Bretagne ( UEB ) -ENSTA Bretagne-Institut Mines-Télécom [Paris]-Centre National de la Recherche Scientifique ( CNRS ) -École Nationale d'Ingénieurs de Brest ( ENIB ) -Université de Bretagne Sud ( UBS ) -Université de Brest ( UBO ) -Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques ( IBNM ), Université de Brest ( UBO ) -Université européenne de Bretagne ( UEB ) -ENSTA Bretagne-Institut Mines-Télécom [Paris]-Centre National de la Recherche Scientifique ( CNRS ), Equipe de Recherche en Ingénierie des Connaissances ( ERIC ), Université Lumière - Lyon 2 ( UL2 ), College of Information Technology (CIT), Can Tho University [Vietnam] (CTU), Département Logique des Usages, Sciences sociales et Sciences de l'Information (LUSSI), Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT), Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Equipe de Recherche en Ingénierie des Connaissances (ERIC), and Université Lumière - Lyon 2 (UL2)
Subjects: Random Forest of Oblique Decision Trees, business.industry, Computer science, Fingerprint classification, Fingerprint (computing), Decision tree, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Oblique case, Scale-invariant feature transform, Pattern recognition, Random forest, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Bag-of-words model in computer vision, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Computer Science::Computer Vision and Pattern Recognition, [ INFO.INFO-TI ] Computer Science [cs]/Image Processing, Noise (video), Artificial intelligence, business, Bag of visual words, [ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI], Rotation (mathematics), Scale-Invariant Feature Transform, [ STAT.ML ] Statistics [stat]/Machine Learning [stat.ML]
Abstract: International audience; Classifying fingerprint images may require an important features extraction step. The Scale-invariant feature transform which extracts local descriptors from images is robust to image scale, rotation and also to changes in illumination, noise, etc. It allows to represent an image in term of the comfortable bag-of-visual-words. This representation leads to a very large number of dimensions. In this case random forest of oblique decision trees are very efficient for a small number of classes. However, in fingerprint classification there are as many classes as individuals. A multi-class version of random forest of oblique decision trees is thus proposed. The numerical tests on seven real datasets (up to 5000 dimensions and 389 classes) show that our proposal has very high accuracy and outperforms state-of-the-art algorithms.
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17. UdL at SemEval-2017 Task 1: Semantic Textual Similarity Estimation of English Sentence Pairs Using Regression Model over Pairwise Features

Author: Lucie Martinet, Fabrice Muhlenbach, Djamel A. Zighed, Hussein T. Al-Natsheh, Entrepôts, Représentation et Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, Dynamic Networks : Temporal and Structural Capture Approach (DANTE), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de l'Informatique du Parallélisme (LIP), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut Rhône-Alpin des systèmes complexes (IXXI), École normale supérieure de Lyon (ENS de Lyon)-Université Lumière - Lyon 2 (UL2)-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Université Lumière - Lyon 2 (UL2)-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Laboratoire Hubert Curien (LHC), Institut d'Optique Graduate School (IOGS)-Université Jean Monnet - Saint-Étienne (UJM)-Centre National de la Recherche Scientifique (CNRS), Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2), Centre National de la Recherche Scientifique (CNRS)-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École normale supérieure - Lyon (ENS Lyon)-Centre National de la Recherche Scientifique (CNRS)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École normale supérieure - Lyon (ENS Lyon)-Institut Rhône-Alpin des systèmes complexes (IXXI), École normale supérieure - Lyon (ENS Lyon)-Université Lumière - Lyon 2 (UL2)-Université Jean Moulin - Lyon 3 (UJML), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Université Lumière - Lyon 2 (UL2)-Université Jean Moulin - Lyon 3 (UJML), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Laboratoire Hubert Curien [Saint Etienne] (LHC), and Université Jean Monnet [Saint-Étienne] (UJM)-Centre National de la Recherche Scientifique (CNRS)-Institut d'Optique Graduate School (IOGS)
Subjects: Word embedding, Computer science, 02 engineering and technology, 050905 science studies, Semantics, computer.software_genre, Semantic similarity, Similarity (network science), 0202 electrical engineering, electronic engineering, information engineering, Feature (machine learning), ComputingMilieux_MISCELLANEOUS, business.industry, 05 social sciences, Pattern recognition, 16. Peace & justice, Ensemble learning, SemEval, [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, ComputingMethodologies_PATTERNRECOGNITION, Bag-of-words model, 020201 artificial intelligence & image processing, Artificial intelligence, 0509 other social sciences, business, computer, Sentence, Natural language processing
Abstract: This paper describes the model UdL we proposed to solve the semantic textual similarity task of SemEval 2017 workshop. The track we participated in was estimating the semantics relatedness of a given set of sentence pairs in English. The best run out of three submitted runs of our model achieved a Pearson correlation score of 0.8004 compared to a hidden human annotation of 250 pairs. We used random forest ensemble learning to map an expandable set of extracted pairwise features into a semantic similarity estimated value bounded between 0 and 5. Most of these features were calculated using word embedding vectors similarity to align Part of Speech (PoS) and Name Entities (NE) tagged tokens of each sentence pair. Among other pairwise features, we experimented a classical tf-idf weighted Bag of Words (BoW) vector model but with character-based range of n-grams instead of words. This sentence vector BoW-based feature gave a relatively high importance value percentage in the feature importances analysis of the ensemble learning.
Published: 2017

18. Evolutionary clustering for categorical data using parametric links among multinomial mixture models

Author: Stéphane Bonnevay, Julien Velcin, Abul Hasnat, Julien Jacques, Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2), Entrepôts, Représentation et Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, Laboratoire Paul Painlevé - UMR 8524 (LPP), Université de Lille-Centre National de la Recherche Scientifique (CNRS), MOdel for Data Analysis and Learning (MODAL), Laboratoire Paul Painlevé (LPP), Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Evaluation des technologies de santé et des pratiques médicales - ULR 2694 (METRICS), Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-École polytechnique universitaire de Lille (Polytech Lille), ANR-12-CORD-0002,ImagiWeb,Images sur le Web : analyse de la dynamique des images sur le Web 2.0.(2012), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Paul Painlevé - UMR 8524 (LPP), Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Evaluation des technologies de santé et des pratiques médicales - ULR 2694 (METRICS), and Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Université de Lille-École polytechnique universitaire de Lille (Polytech Lille)-Université de Lille, Sciences et Technologies
Subjects: Statistics and Probability, Clustering high-dimensional data, Economics and Econometrics, Fuzzy clustering, Twitter data, Correlation clustering, Conceptual clustering, 02 engineering and technology, computer.software_genre, Machine learning, 01 natural sciences, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], evolutionary clustering, 010104 statistics & probability, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], CURE data clustering algorithm, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], 0202 electrical engineering, electronic engineering, information engineering, 0101 mathematics, Cluster analysis, Categorical variable, ComputingMilieux_MISCELLANEOUS, Mathematics, mixture model, multinomial distribution, business.industry, [INFO.INFO-WB]Computer Science [cs]/Web, model-based clustering, Determining the number of clusters in a data set, [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, ComputingMethodologies_PATTERNRECOGNITION, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], 020201 artificial intelligence & image processing, Evolutionary clustering, Multinomial distribution, Mixture model, Model-based clustering, Data mining, Artificial intelligence, Statistics, Probability and Uncertainty, business, computer
Abstract: International audience; In this paper, we propose a novel evolutionary clustering method for temporal categorical data based on parametric links among multinomial mixture models. Besides clustering, our main goal is to interpret the evolutions of clusters over time. To this aim, first we propose the formulation of a generalized model that establishes parametric links among two multinomial mixture. Afterward, different parametric sub-models are defined in order to model typical evolutions of the clustering structure. Model selection criteria allow to select the best sub-models and thus to guess the clustering evolution.For the experiments, first we evaluate the proposed method with synthetic temporal data. Next, we apply it to analyze the annotated social media data. Results show that the proposed method is better than the state-of-the-art based on the common evaluation metrics. Additionally, it can provide interpretation about the temporal evolution of the clusters.
Published: 2017
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19. Data Alteration: A Better Approach to Securing Cloud Data with Encryption

Author: Rhazlane, Sara, Ouazzani, Amina, Harbi, Nouria, Kabachi, Nadia, Badir, Hassan, Rhazlane, Sara, Entrepôts, Représentation et Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), and Université de Lyon-Université Lumière - Lyon 2 (UL2)
Subjects: [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], [INFO.INFO-MA] Computer Science [cs]/Multiagent Systems [cs.MA], ComputingMilieux_MISCELLANEOUS, [INFO.INFO-CR] Computer Science [cs]/Cryptography and Security [cs.CR]
Abstract: International audience; With the emergence of new technologies and the ubiquitous connecti-vity, large amounts of data are being generated everyday with the need to be stored properly and explored rapidly. In this context, the cloud computing services have been adopted to face these rising challenges. But in a cloud environment, data and the application are controlled by the service provider and the customer does not always have the possibility to increase the security level imposed. This leads users to apply encryption mechanisms before storing their data in the cloud. In this paper we propose a new approach that combines the strengths of both steganography and cryptography called Data Alteration. The technique aims to hide the data by modifying it completely as it remains readable, meaningful and therefore shows no suspicions to malicious cloud providers and pirates. The proposed appoach was implemented in Java and tested on realistic datasets in a multi agent systems based architecture.; Avec l’émergence de nouvelles technologies et la connectivité omniprésente, de grandes quantités de données sont générées chaque jour, il est nécessaire de les stocker correctement et de les explorer rapidement. Dans ce contexte, les services de cloud computing ont été adoptés pour faire face à ces défis croissants. Mais dans un environnement en nuage, les données et l'application sont contrôlées par le fournisseur de services et le client n'a pas toujours la possibilité d'augmenter le niveau de sécurité imposé. Cela conduit les utilisateurs à appliquer des mécanismes de cryptage avant de stocker leurs données dans le cloud. Dans cet article, nous proposons une nouvelle approche combinant les forces de la stéganographie et de la cryptographie appelée altération des données. La technique vise à masquer les données en les modifiant complètement, car elles restent lisibles, ont un sens et ne montrent donc aucune suspicion aux fournisseurs de cloud et aux pirates malveillants. L’application proposée a été mise en œuvre en Java et testée sur des ensembles de données réalistes dans une architecture à systèmes multi-agents.
Published: 2017

20. Data Alteration: A Better Approach to Securing Cloud Data with Encryption

Author: Rhazlane, Sara, Ouazzani, Amina, Harbi, Nouria, Kabachi, Nadia, Badir, Hassan, EL ouazzani, Amina, Entrepôts, Représentation et Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), and Université de Lyon-Université Lumière - Lyon 2 (UL2)
Subjects: [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], [INFO.INFO-CR] Computer Science [cs]/Cryptography and Security [cs.CR]
Abstract: With the emergence of new technologies and the ubiquitous connecti-vity, large amounts of data are being generated everyday with the need to be stored properly and explored rapidly. In this context, the cloud computing services have been adopted to face these rising challenges. But in a cloud environment, data and the application are controlled by the service provider and the customer does not always have the possibility to increase the security level imposed. This leads users to apply encryption mechanisms before storing their data in the cloud. In this paper we propose a new approach that combines the strengths of both steganography and cryptography called Data Alteration. The technique aims to hide the data by modifying it completely as it remains readable, meaningful and therefore shows no suspicions to malicious cloud providers and pirates. The proposed appoach was implemented in Java and tested on realistic datasets in a multi agent systems based architecture., Avec l’émergence de nouvelles technologies et la connectivité omniprésente, de grandes quantités de données sont générées chaque jour, il est nécessaire de les stocker correctement et de les explorer rapidement. Dans ce contexte, les services de cloud computing ont été adoptés pour faire face à ces défis croissants. Mais dans un environnement en nuage, les données et l'application sont contrôlées par le fournisseur de services et le client n'a pas toujours la possibilité d'augmenter le niveau de sécurité imposé. Cela conduit les utilisateurs à appliquer des mécanismes de cryptage avant de stocker leurs données dans le cloud. Dans cet article, nous proposons une nouvelle approche combinant les forces de la stéganographie et de la cryptographie appelée altération des données. La technique vise à masquer les données en les modifiant complètement, car elles restent lisibles, ont un sens et ne montrent donc aucune suspicion aux fournisseurs de cloud et aux pirates malveillants. L’application proposée a été mise en œuvre en Java et testée sur des ensembles de données réalistes dans une architecture à systèmes multi-agents.
Published: 2017

21. ECSTRA-INSERM @ CLEF eHealth2016-task 2: ICD10 Code Extraction from Death Certificates

Author: Dermouche, Mohamed, Looten, Vincent, Flicoteaux, Rémi, Chevret, Sylvie, VELCIN, Julien, Taright, Namik, Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2), Université Pierre et Marie Curie - Paris 6 (UPMC), ORS PACA, Biostatistique et épidemiologie clinique, Université Paris Diderot - Paris 7 (UPD7)-Institut National de la Santé et de la Recherche Médicale (INSERM), Entrepôts, Représentation et Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, Service de médecine interne [Saint-Antoine], Université Pierre et Marie Curie - Paris 6 (UPMC)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Saint-Antoine [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU), Velcin, Julien, CHU Saint-Antoine [AP-HP], and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)
Subjects: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-TT] Computer Science [cs]/Document and Text Processing, cause of death extraction, text mining, ICD10 code assignment, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, topic models, machine learning, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], natural language processing
Abstract: International audience; This paper describes the participation of ECSTRA-INSERM team at CLEF eHealth 2016, task 2.C. The task involves extracting ICD10 codes from death certificates, mainly described with short plain texts. We cast the task as a machine learning problem involving the prediction of the ICD10 codes (categorical variable) from the raw text transformed into a bag-of-words matrix. We rely on probabilistic topic models that we evaluate against classical classifiers such as SVM and Naive Bayes. We demonstrate the effectiveness of topic models for this task in terms of prediction accuracy and result interpretation.
Published: 2016

22. Complex organic molecules toward low-mass and high-mass star forming regions

Author: Favre, C., Ceccarelli, C., Bertrand Lefloch, Bergin, E., Carvajal, M., Brouillet, N., Despois, D., Jørgensen, J., Kleiner, I., Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2), Karlsruhe Institute of Technology (KIT), Department of Astronomy [Ann Arbor], University of Michigan [Ann Arbor], University of Michigan System-University of Michigan System, FORMATION STELLAIRE 2016, Laboratoire d'Astrophysique de Bordeaux [Pessac] (LAB), Université de Bordeaux (UB)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), foreign laboratories (FL), CERN [Genève], Laboratoire Interuniversitaire des Systèmes Atmosphériques (LISA (UMR_7583)), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS), Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), and Pomies, Marie-Paule
Subjects: astrochemistry, [SDU.ASTR.GA]Sciences of the Universe [physics]/Astrophysics [astro-ph]/Galactic Astrophysics [astro-ph.GA], [SDU.ASTR.GA] Sciences of the Universe [physics]/Astrophysics [astro-ph]/Galactic Astrophysics [astro-ph.GA], Submillimeter: ISM, ISM: molecules
Abstract: International audience; One of the most important questions in molecular astrophysics is how, when, and where complex organic molecules, COMs (≥ 6 atoms) are formed. In the Interstellar-Earth connection context, could this have a bearing on the origin of life on Earth? Formation mechanisms of COMs, which include potentially prebiotic molecules, are still debated and may include grain-mantle and/or gas-phase chemistry. Understanding the mechanisms that lead to the interstellar molecular complexification, along with the involved physicochemical processes, is mandatory to answer the above questions. In that context, active researches are ongoing in theory, laboratory experiment, chemical modeling and observations. Thanks to recent progress in radioastronomy instrumentation for both single-dish and millimeter array (e.g. Herschel, NOEMA, ALMA), new results have been obtained. I will review some notable results on the detection of COMs, including prebiotic molecules, towards star forming regions.
Published: 2016

23. OLAP Cube-based Graph Approach for Bibliographic Data

Author: Jakawat, Wararat, Favre, Cécile, Loudcher, Sabine, Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2), and Loudcher, Sabine
Subjects: Graph OLAP, [SHS] Humanities and Social Sciences, bibliometrics, [SHS]Humanities and Social Sciences
Abstract: International audience; There is a growing number of dierent research elds that are concerned with bibliographic data analysis. In many cases, data of interest can be described as heterogeneous information networks. To explore knowledge from that networks in a multidimensinal way, OLAP (Online Analytical Processing) analysis helps users to access data from dierent points of views. The ability of OLAP for analyzing classical data is clear. However it must be adapted to provide networked data by considering both nodes and edges. In order to take into account linked data in OLAP on networks, we propose a conceptual graph model to represent bibliographic networks. Then we propose graphs enriched by cubes. Each node and edge of the considered network are described by a cube. It allows the user to quickly analyze the information summarized into cubes. Our proposal also solves the slowly changing dimension problem in OLAP analysis. To illustrate our approach, we integrate three bibliographic databases and a computation process is dened according to user's needs. Then our implementation shows results on a real data.
Published: 2016

24. Generalized Sugeno Integrals

Author: Bruno Teheux, Didiser Dubois, Agnès Rico, Henri Prade, Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), Université Claude Bernard-Lyon I - UCBL (FRANCE), University of Luxembourg (LUXEMBOURG), Institut National Polytechnique de Toulouse - INPT (FRANCE), Argumentation, Décision, Raisonnement, Incertitude et Apprentissage (IRIT-ADRIA), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Centre National de la Recherche Scientifique (CNRS), Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2), and University of Luxembourg [Luxembourg]
Subjects: Pure mathematics, Sugeno integral, Scale (descriptive set theory), Context (language use), 02 engineering and technology, Computer Science::Artificial Intelligence, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Negation, Chain (algebraic topology), [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], 0202 electrical engineering, electronic engineering, information engineering, Calculus, Implications, Commutative property, Mathematics, Logique en informatique, 05 social sciences, 050301 education, [INFO.INFO-LO]Computer Science [cs]/Logic in Computer Science [cs.LO], Informatique et langage, Intelligence artificielle, Apprentissage, Conjunction (grammar), Multifactorial evaluation, Bounded function, 020201 artificial intelligence & image processing, 0503 education, Contraposition (traditional logic), Conjunctions
Abstract: © Springer International Publishing Switzerland 2016. Sugeno integrals are aggregation functions defined on a qualitative scale where only minimum, maximum and order-reversing maps are allowed. Recently, variants of Sugeno integrals based on Gödel implication and its contraposition were defined and axiomatized in the setting of bounded chain with an involutive negation. This paper proposes a more general approach. We consider totally ordered scales, multivalued conjunction operations not necessarily commutative, and implication operations induced from them by means of an involutive negation. In such a context, different Sugeno-like integrals are defined and axiomatized.
Published: 2016

25. Business intelligence indicators : Types, models and implementation

Author: Omar Boussaid, Michel Schneider, Sandro Bimonte, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Université de Clermond-Ferrand 2, Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2), Technologies et systèmes d'information pour les agrosystèmes (UR TSCF), Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes (LIMOS), Ecole Nationale Supérieure des Mines de St Etienne (ENSM ST-ETIENNE)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS), Irstea Publications, Migration, and Ecole Nationale Supérieure des Mines de St Etienne-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS)
Subjects: [SDE] Environmental Sciences, Knowledge management, Computer science, Formalism (philosophy), 02 engineering and technology, 020204 information systems, OLTP, 0202 electrical engineering, electronic engineering, information engineering, ComputingMilieux_MISCELLANEOUS, BUSINESS INTELLIGENCE, OLAP, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], business.industry, Online analytical processing, Uml profile, Conceptual framework, Hardware and Architecture, Business intelligence, Chaining, [SDE]Environmental Sciences, Online transaction processing, 020201 artificial intelligence & image processing, business, Software engineering, Software, Conceptual level
Abstract: [Departement_IRSTEA]Ecotechnologies [TR1_IRSTEA]MOTIVE [Departement_IRSTEA]Ecotechnologies [TR1_IRSTEA]MOTIVE; International audience; Nowadays, more and more data are available for decisional analysis and decision-making based on different indicators. Although different decision-making technologies have been developed, we note the lack of a conceptual framework for the definition and implementation of these indicators. In this paper, we propose a first classification of these indicators. Furthermore, motivated by the need for formalism for the definition of these indicators at a conceptual level, we present the Business Intelligence Indicators (BI2) UML profile to represent indicators for OLAP, OLTP and streaming technologies. We also present their implementation in existing industrial tools. In addition, we show how these indicators can coexist in the same environment to exchange data through a chaining model and its implementation.
Published: 2016

26. OLAP Textual Aggregation Approach using the Google Similarity Distance

Author: Sabile Loudcher, Mustapha Bouakkaz, Youcef Ouinten, Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2), and Université Amar Telidji - Laghouat
Subjects: Decision support system, Textual Aggregation, Information Systems and Management, Computer science, media_common.quotation_subject, 02 engineering and technology, Similarity distance, computer.software_genre, Management Information Systems, [SHS]Humanities and Social Sciences, Semantic similarity, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, High level analysis, Function (engineering), K-means, media_common, Information retrieval, OLAP, Online analytical processing, Google Similarity, k-means clustering, Data warehouse, 020201 artificial intelligence & image processing, Data mining, Statistics, Probability and Uncertainty, computer
Abstract: International audience; Data warehousing and On-Line Analytical Processing (OLAP) are essential elements to decision support. In the case of textual data, decision support requires new tools, mainly textual aggregation functions, for better and faster high level analysis and decision making. Such tools will provide textual measures to users who wish to analyse documents online. In this paper, we propose a new aggregation function for textual data in an OLAP context based on the K-means method. This approach will highlight aggregates semantically richer than those provided by classical OLAP operators. The distance used in K-means is replaced by the Google similarity distance which takes into account the semantic similarity of keywords for their aggregation. The performance of our approach is analyzed and compared to other methods such as Topkeywords, TOPIC, TuBE and BienCube. The experimental study shows that our approach achieves better performances in terms of recall, precision,F-measure complexity and runtime.
Published: 2016

27. Artificial Evolution 2015

Author: Bonnevay, Stephane, Legrand, Pierrick, Monmarché, Nicolas, Lutton, Evelyne, Schoenauer, Marc, Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Quality control and dynamic reliability (CQFD), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Laboratoire d'Informatique Fondamentale et Appliquée de Tours (LIFAT), Université de Tours-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Génie et Microbiologie des Procédés Alimentaires (GMPA), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Machine Learning and Optimisation (TAO), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Université de Tours (UT)-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL), Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Paris-Sud - Paris 11 (UP11)-Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec, Centre National de la Recherche Scientifique (CNRS)-Université de Tours-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL), and Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)
Subjects: [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract: International audience; This book constitutes the thoroughly refereed post-conference proceedings of the 12th International Conference on Artificial Evolution, EA 2015, held in Lyon, France, in October 2015.The 18 revised papers were carefully reviewed and selected from 31 submissions. The focus of the conference is on following topics: Evolutionary Computation, Evolutionary Optimization, Co-evolution, Artificial Life, Population Dynamics, Theory, Algorithmics and Modeling, Implementations, Application of Evolutionary Paradigms to the Real World, Dynamic Optimization, Machine Learning and hybridization with other soft computing techniques.
Published: 2016
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28. TLabel: A New OLAP Aggregation Operator in Text Cubes

Author: Fadila Bentayeb, Omar Boussaid, Lamia Oukid, Nadjia Benblidia, Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2), Département d'Electronique [Blida], and Université de Saâd Dahlab [Blida] (USDB )
Subjects: Class (computer programming), Information retrieval, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Computer science, business.industry, Online analytical processing, 02 engineering and technology, computer.software_genre, 01 natural sciences, Data warehouse, 010104 statistics & probability, Range (mathematics), Text categorization, Operator (computer programming), Text mining, Hardware and Architecture, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Feature (machine learning), Data mining, 0101 mathematics, business, computer, Software, ComputingMilieux_MISCELLANEOUS
Abstract: Data Warehousing technologies and On-Line Analytical Processing (OLAP) feature a wide range of techniques for the analysis of structured data. However, these techniques are inadequate when it comes to analyzing textual data. Indeed, classical aggregation operators have earned their spurs in the online analysis of numerical data, but are unsuitable for the analysis of textual data. To alleviate this shortcoming, on-line analytical processing in text cubes requires new analysis operators adapted to textual data. In this paper, the authors propose a new aggregation operator named Text Label (TLabel), based on text categorization. Their operator aggregates textual data in several classes of documents. Each class is associated with a label that represents the semantic content of the textual data of the class. TLabel is founded on a tailoring of text mining techniques to OLAP. To validate their operator, the authors perform an experimental study and the preliminary results show the interest of their approach for Text OLAP.
Published: 2016

29. Classification model for performance diagnosis of dry port by rail

Author: Makkhongkaew, Raywat, Bonnevay, Stéphane, Aussem, Alex, Benabdeslem, Khalid, Data Mining and Machine Learning (DM2L), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2), and Makkhongkaew, Raywat
Subjects: [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Classifier ensembles, Ensemble learning, Dry port, Empirical performance comparison, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML]
Abstract: International audience; Dry port is an inland intermodal terminal directly linked by road or rail to a seaport as a center for the transshipment of sea freight to inland destinations. However roads contribute to pollution more than rail. For sustainable transportation systems, we need to improve the performance of rail dry port systems. The purpose of this research is to propose a diagnostic system for modeling and classification of rail dry port performance, by use of supervised ensemble learning approaches. We propose an empirical comparison of supervised ensemble learning approaches and application for performance diagnosis of dry port by rail. Our results demonstrate that the performance testing of proposed diagnostic system is found to be very satisfactory. The calculation methods demonstrating the results of the data are provided in this study diagnosis of dry port by rail. Finally, the conclusions for classification modeling for dry port by State Railway of Thailand (SRT) are given.
Published: 2015

30. Elicitation of 2-additive bi-capacity parameters

Author: Julien Ah-Pine, Brice Mayag, Antoine Rolland, Entrepôts, Représentation et Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision (LAMSADE), Université Paris Dauphine-PSL, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2), and Université Paris Dauphine-PSL-Centre National de la Recherche Scientifique (CNRS)
Subjects: Statistics and Probability, Mathematical optimization, Optimization problem, business.industry, Applied Mathematics, General Decision Sciences, Function (mathematics), [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], Bipolar Choquet integral, Computational Mathematics, Character (mathematics), Choquet integral, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], If and only if, Simple (abstract algebra), [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Business, Management and Accounting (miscellaneous), 2-Additive bi-capacity identification, Preference elicitation, Artificial intelligence, Set (psychology), business, Mathematics
Abstract: International audience; In some multi-criteria decision-making problems, it is more convenient to express the decision maker (DM) preferences in bipolar scales. In such cases, the bipolar Choquet integral with respect to bi-capacities was introduced as a versatile tool to model these kind of preferences. However, this aggregation function is useful in practice only if its parameters can be set up easily. To this end, elicitation techniques aim at finding the parameters values that best fit some given examples. In this paper, we address the problem of eliciting a bipolar Choquet integral with respect to a 2-additive bi-capacity. We present several techniques based on solving an optimization problem, taking into account the possible interaction, or not, with the DM. We deal with possible inconsistencies in the observed preferences and we also discuss the parsimonious character of the different models to favor simple models when several solutions exist.
Published: 2015
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31. Simultaneous Clustering and Model Selection for Multinomial Distribution: A Comparative Study

Author: Md. Abul Hasnat, Stéphane Bonnevay, Julien Jacques, Julien Velcin, Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2), MOdel for Data Analysis and Learning (MODAL), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Paul Painlevé - UMR 8524 (LPP), Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Evaluation des technologies de santé et des pratiques médicales - ULR 2694 (METRICS), Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Université de Lille-École polytechnique universitaire de Lille (Polytech Lille)-Université de Lille, Sciences et Technologies, ANR-12-CORD-0002,ImagiWeb,Images sur le Web : analyse de la dynamique des images sur le Web 2.0.(2012), Laboratoire Paul Painlevé (LPP), Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Evaluation des technologies de santé et des pratiques médicales - ULR 2694 (METRICS), Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-École polytechnique universitaire de Lille (Polytech Lille), Jacques, Julien, and Contenus et Interactions - Images sur le Web : analyse de la dynamique des images sur le Web 2.0. - - ImagiWeb2012 - ANR-12-CORD-0002 - CONTINT - VALID
Subjects: FOS: Computer and information sciences, Computer science, Computation, Model selection, Initialization, Machine Learning (stat.ML), [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], computer.software_genre, Stability (probability), Machine Learning (cs.LG), Hierarchical clustering, Methodology (stat.ME), Computer Science - Learning, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Statistics - Machine Learning, Multinomial distribution, Data mining, Cluster analysis, [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], computer, Statistics - Methodology
Abstract: In this paper, we study different discrete data clustering methods, which use the Model-Based Clustering (MBC) framework with the Multinomial distribution. Our study comprises several relevant issues, such as initialization, model estimation and model selection. Additionally, we propose a novel MBC method by efficiently combining the partitional and hierarchical clustering techniques. We conduct experiments on both synthetic and real data and evaluate the methods using accuracy, stability and computation time. Our study identifies appropriate strategies to be used for discrete data analysis with the MBC methods. Moreover, our proposed method is very competitive w.r.t. clustering accuracy and better w.r.t. stability and computation time., Accepted in the International Symposium on Intelligent Data Analysis (IDA 2015)
Published: 2015

32. GOTA: Using the Google Similarity Distance for OLAP Textual Aggregation

Author: Youcef Ouinten, Sabine Loudcher, Mustapha Bouakkaz, Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2), and Loudcher, Sabine
Subjects: Multidimensional analysis, Information retrieval, OLAP, [SHS.STAT]Humanities and Social Sciences/Methods and statistics, Computer science, Online analytical processing, k-means clustering, Context (language use), Unstructured data, Google Similrity, computer.software_genre, Data warehouse, Semantic similarity, [SHS.STAT] Humanities and Social Sciences/Methods and statistics, Data mining, Textual Data, Cluster analysis, computer, Aggregation Function
Abstract: International audience; With the tremendous growth of unstructured data in the Business Intelligence, there is a need for incorporating textual data into data warehouses, to provide an appropriate multidimensional analysis (OLAP) and develop new approaches that take into account the textual content of data. This will provide textual measures to users who wish to analyse documents online. In this paper, we propose a new aggregation function for textual data in an OLAP context. For aggregating keywords, our contribution is to use a data mining technique, such as kmeans, but with a distance based on the Google similarity distance. Thus our approach considers the semantic similarity of keywords for their aggregation. The performance of our approach is analyzed and compared to another method using the k-bisecting clustering algorithm and based on the Jensen-Shannon divergence for the probability distributions. The experimental study shows that our approach achieves better performances in terms of recall, precision,F-measure complexity and runtime.
Published: 2015

33. Representing qualitative capacities as families of possibility measures

Author: Henri Prade, Agnès Rico, Didier Dubois, Argumentation, Décision, Raisonnement, Incertitude et Apprentissage (IRIT-ADRIA), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Centre National de la Recherche Scientifique (CNRS), Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2), Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - INPT (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), Université Claude Bernard-Lyon I - UCBL (FRANCE), Université Lumière-Lyon 2 (FRANCE), Rico, Fabien, and Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Subjects: 0209 industrial biotechnology, Class (set theory), Monotonic function, 02 engineering and technology, [INFO.INFO-DM]Computer Science [cs]/Discrete Mathematics [cs.DM], Measure (mathematics), Upper and lower bounds, Theoretical Computer Science, 020901 industrial engineering & automation, Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, Fuzzy measures, Mathematics, Discrete mathematics, Logique en informatique, Possibility theory, Sequence, Neighborhood semantics, Fuzzy measure theory, Applied Mathematics, Modal logic, Informatique et langage, Intelligence artificielle, Imprecise probability, Apprentissage, [INFO.INFO-DM] Computer Science [cs]/Discrete Mathematics [cs.DM], 020201 artificial intelligence & image processing, Software
Abstract: This paper studies the structure of qualitative capacities, that is, monotonic set-functions, when they range on a finite totally ordered scale equipped with an order-reversing map. These set-functions correspond to general representations of uncertainty, as well as importance levels of groups of criteria in multiple-criteria decision-making. We show that any capacity or fuzzy measure ranging on a qualitative scale can be viewed both as the lower bound of a set of possibility measures and the upper bound of a set of necessity measures (a situation somewhat similar to the one of quantitative capacities with respect to imprecise probability theory). We show that any capacity is characterized by a non-empty class of possibility measures having the structure of an upper semi-lattice. The lower bounds of this class are enough to reconstruct the capacity, and the number of them is characteristic of its complexity. An algorithm is provided to compute the minimal set of possibility measures dominating a given capacity. This algorithm relies on the representation of the capacity by means of its qualitative Mobius transform, and the use of selection functions of the corresponding focal sets. We provide the connection between Sugeno integrals and lower possibility measures. We introduce a sequence of axioms generalizing the maxitivity property of possibility measures, and related to the number of possibility measures needed for this reconstruction. In the Boolean case, capacities are closely related to non-regular modal logics and their neighborhood semantics can be described in terms of qualitative Mobius transforms. A parallel between qualitative monotonic set-functions and imprecise probability.A capacity is an upper necessity measure and a lower possibility measure.Sugeno integral is a lower possibility integral.A bridge between capacities and the neighborhood semantics of non-regular modal logics.
Published: 2015

34. A Novel Multi-Secret Sharing Approach for Secure Data Warehousing and On-Line Analysis Processing in the Cloud

Author: Varunya Attasena, Nouria Harbi, Jérôme Darmont, Entrepôts, Représentation et Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, Kasetsart University - KU (THAILAND), Kasetsart University (KU), Rico, Fabien, Equipe de Recherche en Ingénierie des Connaissances (ERIC), and Université Lumière - Lyon 2 (UL2)
Subjects: FOS: Computer and information sciences, Information privacy, Computer Science - Cryptography and Security, Data warehouses, Computer science, Distributed computing, Cloud computing, 02 engineering and technology, Secret sharing, [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], Computer Science - Databases, Return on investment, 020204 information systems, Data integrity, 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB], ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT, OLAP, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], business.industry, Databases (cs.DB), Business agility, Data warehouse, Data availability, Hardware and Architecture, Star schema, Benchmark (computing), 020201 artificial intelligence & image processing, business, Cryptography and Security (cs.CR), Data privacy, Software
Abstract: Republished from the International Journal of Data Warehousing and Mining, Vol. 11, No. 2, April-June 2015, 21-42; International audience; Cloud computing can help reduce costs, increase business agility and deploy solutions with a high return on investment for many types of applications, including data warehouses and on-line analytical processing. However, storing and transferring sensitive data into the cloud rais-es legitimate security concerns. In this paper, we propose a new multi-secret sharing approach for deploying a data warehouse in the cloud and allowing on-line analysis processing, while enforcing data privacy, integrity and availability. We first validate the relevance of our ap-proach theoretically, and then experimentally with both a simple random dataset and the Star Schema Benchmark. We also demonstrate its superiority to related, existing methods.
Published: 2015
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35. A Joint Model for Topic-Sentiment Modeling from Text

Author: Sabine Loudcher, Leila Kouas, Mohamed Dermouche, Julien Velcin, Velcin, Julien, Loudcher, Sabine, Equipe de Recherche en Ingénierie des Connaissances (ERIC), and Université Lumière - Lyon 2 (UL2)
Subjects: Topic model, [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-WB] Computer Science [cs]/Web, Process (engineering), Computer science, InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL, Inference, [INFO.INFO-TT] Computer Science [cs]/Document and Text Processing, computer.software_genre, symbols.namesake, topic models, Text mining, [SHS.STAT] Humanities and Social Sciences/Methods and statistics, sentiment analysis Keywords Joint topic sentiment models, Text Analysis, G3 [Mathematics of Computing]: Probabil-ity and Statistics-Probabilistic Algorithms General Terms Algorithm, [SHS.STAT]Humanities and Social Sciences/Methods and statistics, Probabilistic latent semantic analysis, business.industry, Sentiment analysis, H33 [Information Systems]: Information Storage and Retrieval, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], sentiment analysis, symbols, opinion mining, Data mining, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], business, Joint (audio engineering), computer, Gibbs sampling
Abstract: International audience; Traditional topic models, like LDA and PLSA, have been efficiently extended to capture further aspects of text in addition to the latent topics (e.g., time evolution, sentiment etc.). In this paper, we discuss the issue of joint topic-sentiment modeling. We propose a novel topic model for topic-specific sentiment modeling from text and we derive an inference algorithm based on the Gibbs sampling process. We also propose a method for automatically setting the model parameters. The experiments performed on two review datasets show that our model outperforms other state-of-the-art models, in particular for sentiment prediction at the topic level.
Published: 2015

36. The Cube of Opposition: A Structure Underlying Many Knowledge Representation Formalisms

Author: Dubois, Didier, Prade, Henri, Rico, Agnès, Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), Université Claude Bernard-Lyon I - UCBL (FRANCE), Université Lumière-Lyon 2 (FRANCE), Argumentation, Décision, Raisonnement, Incertitude et Apprentissage (IRIT-ADRIA), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Centre National de la Recherche Scientifique (CNRS), Equipe de Recherche en Ingénierie des Connaissances (ERIC), and Université Lumière - Lyon 2 (UL2)
Subjects: Logique en informatique, Logic, Informatique et langage, Intelligence artificielle, Apprentissage, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract: International audience; The square of opposition is a structure involving two involutive negations and relating quantified statements, invented in Aristotle time. Redis-covered in the second half of the XX th century,and advocated as being of interest for understanding conceptual structures and solving problems inparaconsistent logics, the square of opposition hasbeen recently completed into a cube, which corresponds to the introduction of a third negation.Such a cube can be encountered in very different knowledge representation formalisms, such asmodal logic, possibility theory in its all-or-nothingversion, formal concept analysis, rough set theoryand abstract argumentation. After restating theseresults in a unified perspective, the paper proposes agraded extension of the cube and shows that severalqualitative, as well as quantitative formalisms, suchas Sugeno integrals used in multiple criteria aggregation and qualitative decision theory, or yet belieffunctions and Choquet integrals, are amenable totransformations that form graded cubes of opposition. This discovery leads to a new perspective onmany knowledge representation formalisms, layingtheir underlying common features. The cube of opposition exhibits fruitful parallelisms between different formalisms, which leads to highlight somemissing components present in one formalism andcurrently absent from another.
Published: 2015

37. Extracting Decision Rules from Qualitative Data Using Sugeno Integral: A Case-Study

Author: Claude Durrieu, Agnès Rico, Henri Prade, Didier Dubois, Yannis Ferro, Centre National de la Recherche Scientifique - CNRS (FRANCE), Ecole Nationale des Travaux Publics de l'Etat - ENTPE (FRANCE), Institut National Polytechnique de Toulouse - INPT (FRANCE), Institut National de la Recherche Agronomique - INRA (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), Université Claude Bernard-Lyon I - UCBL (FRANCE), Université Lumière-Lyon 2 (FRANCE), Argumentation, Décision, Raisonnement, Incertitude et Apprentissage (IRIT-ADRIA), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Centre National de la Recherche Scientifique (CNRS), Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2), Université Toulouse III - Paul Sabatier (UT3), Laboratoire d'Ecologie des Hydrosystèmes Naturels et Anthropisés (LEHNA), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-École Nationale des Travaux Publics de l'État (ENTPE)-Centre National de la Recherche Scientifique (CNRS), Université de Lyon, and Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Subjects: Logique en informatique, Basis (linear algebra), business.industry, [SDV]Life Sciences [q-bio], Logical representation, Sugeno integral, Experimental data, Qualitative property, Informatique et langage, Decision rule, [INFO.INFO-DM]Computer Science [cs]/Discrete Mathematics [cs.DM], Intelligence artificielle, computer.software_genre, Machine learning, Apprentissage, Knowledge extraction, Selection (linguistics), Artificial Intelligence & Image Processing, Data mining, Artificial intelligence, business, computer, Mathematics
Abstract: © Springer International Publishing Switzerland 2015. This paper deals with knowledge extraction from experimental data in multifactorial evaluation using Sugeno integrals. They are qualitative criteria aggregations where it is possible to assign weights to groups of criteria. A method for deriving such weights from data is recalled. We also present results in the logical representation of Sugeno integrals. Then we show how to extract if-then rules expressing the selection of good situations on the basis of local evaluations, and rules to detect bad situations. We illustrate such methods on a case-study in the area of water ecosystem health.
Published: 2015
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38. A Joint Model for Topic-Sentiment Evolution over Time

Author: Julien Velcin, Mohamed Dermouche, Leila Khouas, Sabine Loudcher, Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2), and Loudcher, Sabine
Subjects: Topic model, Text Analysis, [SHS.STAT]Humanities and Social Sciences/Methods and statistics, Process (engineering), Computer science, business.industry, Sentiment analysis, InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL, Inference, computer.software_genre, Data modeling, symbols.namesake, Text mining, [SHS.STAT] Humanities and Social Sciences/Methods and statistics, symbols, Data mining, Opinion Mining, Focus (optics), business, computer, Gibbs sampling
Abstract: International audience; Most existing topic models focus either on extracting static topic-sentiment conjunctions or topic-wise evolution over time leaving out topic-sentiment dynamics and missing the opportunity to provide a more in-depth analysis of textual data. In this paper, we propose an LDA-based topic model for analyzing topic-sentiment evolution over time by modeling time jointly with topics and sentiments. We derive inference algorithm based on Gibbs Sampling process. Finally, we present results on reviews and news datasets showing interpretable trends and strong correlation with ground truth in particular for topic sentiment evolution over time.
Published: 2014

39. Automatic Textual Aggregation Approach of Scientific Articles in OLAP context

Author: Bouakkaz Mustapha, Ouinten Youcef, Loudcher Sabine, Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2), and Loudcher, Sabine
Subjects: Text Analysis, Information retrieval, Olap, [SHS.STAT]Humanities and Social Sciences/Methods and statistics, business.industry, Computer science, Online analytical processing, media_common.quotation_subject, InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL, Context (language use), Data warehouse, Data modeling, Business intelligence, [SHS.STAT] Humanities and Social Sciences/Methods and statistics, Graph (abstract data type), Function (engineering), Cluster analysis, business, media_common
Abstract: International audience; In the last decade, Online Analytical Processing (OLAP) has taken an increasingly important role in Business Intelligence. Approaches, solutions and tools have been provided for both databases and data warehouses, which focus mainly on numerical data. These solutions are not suitable for textual data. Because of the fast growing of this type of data, there is a need for new approaches that take into account the textual content of data. In the context of Text OLAP (OLAP on text or documents), the measure can be textual and need a suitable aggregation function for OLAP operations such as roll-up.We present in this paper a new aggregation function for textual data. Our approach is based on the affinity between keywords and uses the search of cycles in a graph to find the aggregated keywords. We also present performances and a comparison with three other methods. The experimental study shows good results for our approach.
Published: 2014

40. Herschel Observations of Extraordinary Sources

Author: Nuria Marcelino, Maria Kleshcheva, Floris van der Tak, Peter Schilke, G. B. Esplugues, Justin L. Neill, Martin Emprechtinger, Nathan R. Crockett, Rene Plume, Brett A. McGuire, Harshal Gupta, Thomas G. Phillips, Edwin A. Bergin, Steven D. Lord, Tom Bell, Dariusz C. Lis, Belén Tercero, Cécile Favre, José Cernicharo, Shanshan Yu, John C. Pearson, Geoffrey A. Blake, Harvard-Smithsonian Center for Astrophysics (CfA), Harvard University [Cambridge]-Smithsonian Institution, Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2), I. Physikalisches Institut [Köln], Universität zu Köln, Centro de Investigaciones Biológicas (CSIC), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), Jet Propulsion Laboratory (JPL), NASA-California Institute of Technology (CALTECH), Centro de Astrobiologia [Madrid] (CAB), Instituto Nacional de Técnica Aeroespacial (INTA)-Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), Department of Public Health and General Practice, University of Otago [Dunedin, Nouvelle-Zélande], CalTech-CSO (CSO), California Institute of Technology (CALTECH), Department of Physics and Astronomy [Calgary], University of Calgary, SRON Netherlands Institute for Space Research (SRON), Dpt. Astrofisica Molecular e Infrarroja, CSIS - IEM, and Astronomy
Subjects: ISM: individual objects: Orion KL, FOS: Physical sciences, Astrophysics, Astrophysics::Cosmology and Extragalactic Astrophysics, Spectral line, ISM: abundances, law.invention, Telescope, law, Astrophysics::Solar and Stellar Astrophysics, Isotopologue, Spectral resolution, Spectroscopy, ComputingMilieux_MISCELLANEOUS, Astrophysics::Galaxy Astrophysics, Physics, [PHYS]Physics [physics], Nebula, Astronomy and Astrophysics, Astrophysics - Astrophysics of Galaxies, Galaxy, ISM: molecules, 13. Climate action, Space and Planetary Science, Astrophysics of Galaxies (astro-ph.GA), [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], Energy (signal processing)
Abstract: We present a comprehensive analysis of a broad band spectral line survey of the Orion Kleinmann-Low nebula (Orion KL), one of the most chemically rich regions in the Galaxy, using the HIFI instrument on board the Herschel Space Observatory. This survey spans a frequency range from 480 to 1907 GHz at a resolution of 1.1 MHz. These observations thus encompass the largest spectral coverage ever obtained toward this high-mass star-forming region in the sub-mm with high spectral resolution, and include frequencies $>$ 1 THz where the Earth's atmosphere prevents observations from the ground. In all, we detect emission from 39 molecules (79 isotopologues). Combining this dataset with ground based mm spectroscopy obtained with the IRAM 30 m telescope, we model the molecular emission from the mm to the far-IR using the XCLASS program which assumes local thermodynamic equilibrium (LTE). Several molecules are also modeled with the MADEX non-LTE code. Because of the wide frequency coverage, our models are constrained by transitions over an unprecedented range in excitation energy. A reduced $��^{2}$ analysis indicates that models for most species reproduce the observed emission well. In particular, most complex organics are well fit by LTE implying gas densities are high ($>$10$^6$ cm$^{-3}$) and excitation temperatures and column densities are well constrained. Molecular abundances are computed using H$_{2}$ column densities also derived from the HIFI survey. The distribution of rotation temperatures, $T_{\rm rot}$, for molecules detected toward the hot core is significantly wider than the compact ridge, plateau, and extended ridge $T_{\rm rot}$ distributions, indicating the hot core has the most complex thermal structure., Accepted to ApJ. 92 pages, 24 Figures, 9 Tables. Public data products may not be available yet
Published: 2014
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41. Herschel observations of EXtraordinary Sources: Analysis of the full Herschel/HIFI molecular line survey of Sagittarius B2(N)

Author: Edwin A. Bergin, Shanshan Yu, Thomas G. Phillips, Trevor D. McNeill, Peter Schilke, Cécile Favre, Justin L. Neill, Claudia Comito, Martin Emprechtinger, Amanda L. Steber, Dariusz C. Lis, Raquel Monje, Andrew M. Burkhardt, Brent J. Harris, Brett A. McGuire, Dana E. Anderson, Sheng-Li Qin, Steven D. Lord, Jo Hsin Chen, Tatiana Vasyunina, Nathan R. Crockett, Harvard-Smithsonian Center for Astrophysics (CfA), Harvard University [Cambridge]-Smithsonian Institution, I. Physikalisches Institut [Köln], Universität zu Köln, Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2), Laboratoire de Cosmologie, Astrophysique Stellaire & Solaire, de Planétologie et de Mécanique des Fluides (CASSIOPEE), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de la Côte d'Azur, Université Côte d'Azur (UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), CalTech-CSO (CSO), and California Institute of Technology (CALTECH)
Subjects: Physics, [PHYS]Physics [physics], Spectral signature, Thermodynamic equilibrium, Resolution (electron density), FOS: Physical sciences, Astronomy and Astrophysics, Astrophysics, Astrophysics - Astrophysics of Galaxies, Wavelength, 13. Climate action, Space and Planetary Science, Astrophysics of Galaxies (astro-ph.GA), Astrophysics::Solar and Stellar Astrophysics, Sagittarius B2, Isotopologue, Absorption (electromagnetic radiation), [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], ComputingMilieux_MISCELLANEOUS, Astrophysics::Galaxy Astrophysics, Line (formation)
Abstract: A sensitive broadband molecular line survey of the Sagittarius B2(N) star-forming region has been obtained with the HIFI instrument on the Herschel Space Observatory, offering the first high-spectral resolution look at this well-studied source in a wavelength region largely inaccessible from the ground (625-157 um). From the roughly 8,000 spectral features in the survey, a total of 72 isotopologues arising from 44 different molecules have been identified, ranging from light hydrides to complex organics, and arising from a variety of environments from cold and diffuse to hot and dense gas. We present an LTE model to the spectral signatures of each molecule, constraining the source sizes for hot core species with complementary SMA interferometric observations, and assuming that molecules with related functional group composition are cospatial. For each molecule, a single model is given to fit all of the emission and absorption features of that species across the entire 480-1910 GHz spectral range, accounting for multiple temperature and velocity components when needed to describe the spectrum. As with other HIFI surveys toward massive star forming regions, methanol is found to contribute more integrated line intensity to the spectrum than any other species. We discuss the molecular abundances derived for the hot core, where the local thermodynamic equilibrium approximation is generally found to describe the spectrum well, in comparison to abundances derived for the same molecules in the Orion KL region from a similar HIFI survey., Accepted to ApJ. 64 pages, 14 figures. Truncated abstract
Published: 2014
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42. The logical encoding of Sugeno integrals

Author: Henri Prade, Agnès Rico, Didier Dubois, Argumentation, Décision, Raisonnement, Incertitude et Apprentissage (IRIT-ADRIA), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Centre National de la Recherche Scientifique (CNRS), Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2), Centre National de la Recherche Scientifique - CNRS (FRANCE), Ecole Normale Supérieure de Lyon - ENS de Lyon (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), Université Claude Bernard-Lyon I - UCBL (FRANCE), Université Toulouse 1 Capitole (UT1)-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), and Institut National Polytechnique de Toulouse - INPT (FRANCE)
Subjects: Scale (ratio), Logic, Measure (physics), Possibilistic Logic, [INFO.INFO-DM]Computer Science [cs]/Discrete Mathematics [cs.DM], Discrete Sugeno Integrals, Fuzzy logic, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Sugeno integral, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Artificial Intelligence, Encoding (memory), Artificial Intelligence & Image Processing, Possibility Theory, Mathematics, Logique en informatique, [INFO.INFO-LO]Computer Science [cs]/Logic in Computer Science [cs.LO], Informatique et langage, Intelligence artificielle, Propositional calculus, Apprentissage, Algebra, Multiple criteria, Possibilistic logic, Algorithm
Abstract: International audience; Sugeno integrals are a well-known family of qualitative multiple criteria aggregation operators. The paper investigates how the behavior of these operators can be described in a prioritized propositional logic language, namely possibilistic logic. The case of binary-valued criteria, which amounts to providing a logical description of the fuzzy measure underlying the integral, is first considered. The general case of a Sugeno integral when critersia are valued on a discrete scale is then studied.
Published: 2014
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43. 13C-methyl formate: observations of a sample of high mass star-forming regions including Orion-KL and spectroscopic characterization

Author: Nathan R. Crockett, Isabelle Kleiner, D. Despois, Edwin A. Bergin, Cécile Favre, Miguel Carvajal, N. Brouillet, Thérèse R. Huet, Alain Baudry, Jes K. Jørgensen, Suzanne E. Bisschop, David J. Field, Laurent Margulès, Jean Demaison, Justin L. Neill, Université Grenoble Alpes - UFR Langage, lettres et arts du spectacle, information et communication - Dpt Lettres et arts du spectacle (UGA UFR LLASIC LAS), Université Grenoble Alpes (UGA), Université de Bordeaux (UB), Université Sciences et Technologies - Bordeaux 1, FORMATION STELLAIRE 2014, Laboratoire d'astrodynamique, d'astrophysique et d'aéronomie de bordeaux (L3AB), Université Sciences et Technologies - Bordeaux 1-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Sciences et Technologies - Bordeaux 1-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire aquitain des sciences de l'univers (OASU), Université Sciences et Technologies - Bordeaux 1-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire d'Astrophysique de Bordeaux [Pessac] (LAB), Université de Bordeaux (UB)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB), Laboratoire d'Astrophysique de Bordeaux [Pessac] (LAB), Université de Bordeaux (UB)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Université Sciences et Technologies - Bordeaux 1-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Observatoire aquitain des sciences de l'univers (OASU), Laboratoire Jean Alexandre Dieudonné (JAD), Université Nice Sophia Antipolis (... - 2019) (UNS), Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), Génomique Fonctionnelle des Tumeurs Solides (U1162), Université Paris 13 (UP13)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM), Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2), Departamento de Fisica Aplicada, Universidad de Huelva, College of Natural Sciences, Hawaii Pacific University, Leiden Observatory [Leiden], Universiteit Leiden [Leiden], Laboratoire Interuniversitaire des Systèmes Atmosphériques (LISA (UMR_7583)), Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Harvard-Smithsonian Center for Astrophysics (CfA), Harvard University [Cambridge]-Smithsonian Institution, Laboratoire de Physique des Lasers, Atomes et Molécules - UMR 8523 (PhLAM), Université de Lille-Centre National de la Recherche Scientifique (CNRS), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS), Smithsonian Institution-Harvard University [Cambridge], Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS), Université Grenoble Alpes - UFR Langage, lettres et arts du spectacle, information et communication - Dpt Lettres et arts du spectacle ( UGA UFR LLASIC LAS ), Université Grenoble Alpes ( UGA ), Université de Bordeaux ( UB ), Laboratoire d'astrodynamique, d'astrophysique et d'aéronomie de bordeaux ( L3AB ), Université Sciences et Technologies - Bordeaux 1-Institut national des sciences de l'Univers ( INSU - CNRS ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Sciences et Technologies - Bordeaux 1-Institut national des sciences de l'Univers ( INSU - CNRS ) -Centre National de la Recherche Scientifique ( CNRS ) -Observatoire aquitain des sciences de l'univers ( OASU ), Université Sciences et Technologies - Bordeaux 1-Institut national des sciences de l'Univers ( INSU - CNRS ) -Centre National de la Recherche Scientifique ( CNRS ) -Centre National de la Recherche Scientifique ( CNRS ) -Laboratoire d'Astrophysique de Bordeaux [Pessac] ( LAB ), Université de Bordeaux ( UB ) -Institut national des sciences de l'Univers ( INSU - CNRS ) -Centre National de la Recherche Scientifique ( CNRS ) -Université de Bordeaux ( UB ), Laboratoire d'Astrophysique de Bordeaux [Pessac] ( LAB ), Université de Bordeaux ( UB ) -Institut national des sciences de l'Univers ( INSU - CNRS ) -Centre National de la Recherche Scientifique ( CNRS ), Université Sciences et Technologies - Bordeaux 1-Institut national des sciences de l'Univers ( INSU - CNRS ) -Centre National de la Recherche Scientifique ( CNRS ), Observatoire aquitain des sciences de l'univers ( OASU ), Laboratoire Jean Alexandre Dieudonné ( JAD ), Université Nice Sophia Antipolis ( UNS ), Université Côte d'Azur ( UCA ) -Université Côte d'Azur ( UCA ) -Centre National de la Recherche Scientifique ( CNRS ), Genomique Fonctionnelle des Tumeurs Solides, Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Université Paris Descartes - Paris 5 ( UPD5 ) -IFR105-Université Paris Diderot - Paris 7 ( UPD7 ), and Centre National de la Recherche Scientifique (CNRS)-Université de Lille
Subjects: 010504 meteorology & atmospheric sciences, Methyl formate, Star (game theory), Analytical chemistry, FOS: Physical sciences, Astrophysics, 01 natural sciences, laboratory: molecular [methods], Isotopomers, chemistry.chemical_compound, [PHYS.QPHY]Physics [physics]/Quantum Physics [quant-ph], 0103 physical sciences, data analysis [methods], Molecule, Isotopologue, Formate, 010303 astronomy & astrophysics, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, Physics, abundances [ISM], [PHYS.PHYS.PHYS-AO-PH]Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph], Astrophysics of Galaxies, astrochemistry, [SDU.ASTR.SR]Sciences of the Universe [physics]/Astrophysics [astro-ph]/Solar and Stellar Astrophysics [astro-ph.SR], Image (category theory), Astronomy and Astrophysics, Partition function (mathematics), Astrophysics - Astrophysics of Galaxies, [CHIM.THEO]Chemical Sciences/Theoretical and/or physical chemistry, chemistry, 13. Climate action, Space and Planetary Science, Astrophysics of Galaxies (astro-ph.GA), identification [line], spectroscopic [techniques], [PHYS.PHYS.PHYS-CHEM-PH]Physics [physics]/Physics [physics]/Chemical Physics [physics.chem-ph], [ SDU.ASTR.SR ] Sciences of the Universe [physics]/Astrophysics [astro-ph]/Solar and Stellar Astrophysics [astro-ph.SR]
Abstract: We have surveyed a sample of massive star-forming regions located over a range of distances from the Galactic centre for methyl formate, HCOOCH$_{3}$, and its isotopologues H$^{13}$COOCH$_{3}$ and HCOO$^{13}$CH$_{3}$. The observations were carried out with the APEX telescope in the frequency range 283.4--287.4~GHz. Based on the APEX observations, we report tentative detections of the $^{13}$C-methyl formate isotopologue HCOO$^{13}$CH$_{3}$ towards the following four massive star-forming regions: Sgr~B2(N-LMH), NGC~6334~IRS~1, W51 e2 and G19.61-0.23. In addition, we have used the 1~mm ALMA science verification observations of Orion-KL and confirm the detection of the $^{13}$C-methyl formate species in Orion-KL and image its spatial distribution. Our analysis shows that the $^{12}$C/$^{13}$C isotope ratio in methyl formate toward Orion-KL Compact Ridge and Hot Core-SW components (68.4$\pm$10.1 and 71.4$\pm$7.8, respectively) are, for both the $^{13}$C-methyl formate isotopologues, commensurate with the average $^{12}$C/$^{13}$C ratio of CO derived toward Orion-KL. Likewise, regarding the other sources, our results are consistent with the $^{12}$C/$^{13}$C in CO. We also report the spectroscopic characterization, which includes a complete partition function, of the complex H$^{13}$COOCH$_{3}$ and HCOO$^{13}$CH$_{3}$ species. New spectroscopic data for both isotopomers H$^{13}$COOCH$_{3}$ and HCOO$^{13}$CH$_{3}$, presented in this study, has made it possible to measure this fundamentally important isotope ratio in a large organic molecule for the first time., Comment: Accepted for publication in The Astrophysical Journal Supplement Series
Published: 2014
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44. Speed prediction of the sport horse from accelerometric and gyroscopic data

Author: Schmutz, Amandine, Jacques, Julien, Cheze, Laurence, Martin, Pauline, Cadic, Ifsttar, Laboratoire de Biomécanique et Mécanique des Chocs (LBMC UMR T9406), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR), Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2), and CWD
Subjects: BIOMECANIQUE, VITESSE, [SPI.MECA.BIOM] Engineering Sciences [physics]/Mechanics [physics.med-ph]/Biomechanics [physics.med-ph], [SPI.MECA.BIOM]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Biomechanics [physics.med-ph], SPORT HORSE, SPORT
Abstract: 4th International Workshop on Functional and Operatorial Statistics, LA COROGNE, ESPAGNE, 15-/06/2014 - 17/06/2014; With the growth of connected and smart sport devices to improve performance, this kind of object is developed for equestrian sports. An accelerometer and a gyroscope are put on horse withers. It collects data from the 3 space directions (x, y, z) and it allows calculation of parameters that characterize motion like cranio-caudal, medio-lateral and dorsoventral energies or gallop frequency. The objective is to predict the speed of the horse per second with an exactness of 0,6 m/s in order to have a new parameter characteristic of the horse motion.
Published: 2014

45. How to Learn with Naive Bayes and Prior Knowledge: an Application to Sentiment Analysis

Author: Mohamed Dermouche, Leila Khouas, Julien Velcin, Sabine Loudcher, Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2), and Loudcher, Sabine
Subjects: [SHS.STAT]Humanities and Social Sciences/Methods and statistics, [SHS.STAT] Humanities and Social Sciences/Methods and statistics, opinion mining, ComputingMilieux_MISCELLANEOUS
Abstract: International audience
Published: 2013

46. Fusion Cubes: Towards Self-Service Business Intelligence

Author: Torben Bach Pedersen, Alberto Abelló, Jérôme Darmont, Felix Naumann, Lorena Etcheverry, Matteo Golfarelli, Jose-Norberto Mazón, Stefano Rizzi, Juan Trujillo, Panos Vassiliadis, Gottfried Vossen, Universitat Politècnica de Catalunya [Barcelona] (UPC), Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2), Universidad de la República [Montevideo] (UCUR), Alma Mater Studiorum Università di Bologna [Bologna] (UNIBO), Universidad de Alicante, Hasso Plattner Institute [Potsdam, Germany], Aalborg University [Denmark] (AAU), University of Ioannina, Westfälische Wilhelms-Universität Münster (WWU), Dagstuhl Seminar 11361, Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos, Web and Knowledge (WaKe), Lucentia, A. Abelló, J. Darmont, L. Etcheverry, M. Golfarelli, J.N. Mazón, F. Naumann, T. Bach Pedersen, S. Rizzi, J. Trujillo, P. Vassiliadi, and G. Vossen
Subjects: Data warehouses, Computer science, 02 engineering and technology, computer.software_genre, Metadata discovery, Business intelligence, World Wide Web, Metadata quality, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Schema discovery, Data cube, Data element, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], business.industry, Open data, Data fusion, Sensor fusion, Data science, Data warehouse, Metadata repository, Metadata, ETL, Hardware and Architecture, Lenguajes y Sistemas Informáticos, 020201 artificial intelligence & image processing, Data integration, business, computer, Software
Abstract: International audience; Self-service business intelligence is about enabling non-expert users to make well-informed decisions by enriching the decision process with situational data, i.e., data that have a narrow focus on a specific business problem and, typically, a short lifespan for a small group of users. Often, these data are not owned and controlled by the decision maker; their search, extraction, integration, and storage for reuse or sharing should be accomplished by decision makers without any intervention by designers or programmers. The goal of this paper is to present the framework we envision to support self-service business intelligence and the related research challenges; the underlying core idea is the notion of fusion cubes, i.e., multidimensional cubes that can be dynamically extended both in their schema and their instances, and in which situational data and metadata are associated with quality and provenance annotations.
Published: 2013
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47. Spatial correlation in bipartite networks

Author: Villa-Vialaneix, Nathalie, Jouve, Bertrand, Rossi, Fabrice, Hautefeuille, Florent, Vialaneix, Nathalie, Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne) (SAMM), Université Paris 1 Panthéon-Sorbonne (UP1), Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2), Travaux et recherches archéologiques sur les cultures, les espaces et les sociétés (TRACES), and Ministère de la Culture et de la Communication (MCC)-École des hautes études en sciences sociales (EHESS)-Université Toulouse - Jean Jaurès (UT2J)-Centre National de la Recherche Scientifique (CNRS)
Subjects: [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], [STAT.TH] Statistics [stat]/Statistics Theory [stat.TH], [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH], [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST]
Abstract: International audience; In this article, the influence between a spatial information and interactions between individuals is addressed. This issue is illustrated through the analysis of a corpus of notarial acts established during the Middle Ages. In this corpus, the persons interact in common transactions that are geolocalized. The present work tries to quantify the impact of this spatial information on the relations between people. As the spatial information as well as the relations between individuals are derived from the same source (the transactions in which the individuals have been involved), a standard Mantel test (Mantel, 1967) is not suited to address this issue. A similar methodology, based on the adaptation of the original permutation test, is thus proposed and illustrated in that context.
Published: 2013

48. The relationship between corporate social responsibility and innovation activities in SMEs: an empirical study of French firms

Author: Bérard, Céline, Szostak, Berangere, Abdesselam, Rafik, COACTIS (COACTIS), Université Lumière - Lyon 2 (UL2)-Université Jean Monnet [Saint-Étienne] (UJM), Université Lumière - Lyon 2 (UL2), Equipe de Recherche en Ingénierie des Connaissances (ERIC), and Bérard, Céline
Subjects: Clustering and discriminant analysis, SME, Factorial analyses, [SHS.GESTION]Humanities and Social Sciences/Business administration, Exploration, Corporate social responsibility, Exploitation, [SHS.GESTION] Humanities and Social Sciences/Business administration
Abstract: Purpose - A growing body of research has addressed the relationship between CSR and innovation. However, research on this relationship is still at an embryonic stage, and there is a need to better understand and further analyze the complex relationship within the specific context of SMEs, especially since innovation is seen as key to their survival and success. This paper proposes to address this gap in small business literature by studying the relationship between CSR practices and exploration and exploitation. It raises the following question: are SMEs that explore and exploit differently distinguished by the extent of their adoption of CSR practices? Literature - Indeed, we chose to rely not on innovation outputs themselves, but on exploration and exploitation activities (March 1991). The literature has suggested that exploitation may be positively related to incremental innovation performance, and exploration positively related to radical innovation performance (Atuahene-Gima 2005). In fact, radical and incremental innovations can be viewed as outputs of exploration and exploitation respectively (Bierly and Daly 2007). And yet, some authors have suggested that firms that adopt a CSR strategy may introduce innovations that are more incremental than radical (López-Pérez, Perez-Lopez, and Rodriguez-Ariza 2007). In this paper we consider CSR through environmental and human sustainability components; five dimensions were so retained: (1) workplace, (2) environmental policies, (3) market policies, (4) communities policies and (5) company values (see European Commission, 2005). Thus, one may suggest that firms with different CSR practices (in terms of environmental and social practices) explore and exploit differently: SMEs that intensively adopt CSR practices may focus more on exploitation than on exploration. Methodology - A questionnaire was administered to CEOs of firms in the French region Rhône-Alpes. Our final sample includes 488 SMEs located in this region. Statistical analyzes based on a methodological chain of data analysis methods are used to identify homogeneous groups of SMEs with respect to their activities of exploitation and exploration, then to analyze the potential effects of a set of CSR practices on these innovation activities. So, we first classified the firms according to their innovation activities which allowed us to distinguish and identify four groups of firms: "exploitation", "exploration", "exploitation and exploration" and "neither exploitation, nor exploration". We then conducted a detailed statistical description of the contents of each group from "standard" characteristics and CSR practices. Finally, a barycentric discriminant analysis was performed to identify CSR practices that differentiate and well separate these groups. Results and implications - Our findings confirm that not all CSR practices lead to innovation activities, justifying the need to take into account the nature of involved practices. We indeed find that the low adoption of a range of CSR practices is typical of firms in the "exploration" group, while the "exploitation" group is characterized by a higher effort to develop CSR practices, however few in number. Furthermore, the results show that the high adoption of a large range of CSR practices is related to firms in the "exploration and exploitation" group. In fact, while the group of "exploitation and exploration" is more similar to profile "exploration" when considering only innovation activities, it is more similar to profile "exploitation" when considering the CSR practices adopted by SMEs.
Published: 2013

49. A time-varying model to simulate a collective decisional problem

Author: Abdelhak Imoussaten, Jacky Montmain, Fabien Rico, Agnès Rico, Laboratoire de Génie Informatique et Ingénierie de Production (LGI2P), IMT - MINES ALES (IMT - MINES ALES), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2), and CHABANNE, Cécile
Subjects: 050402 sociology, Collective decision, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Social network, Computer science, business.industry, Group (mathematics), 05 social sciences, Control (management), 16. Peace & justice, Preference, 0504 sociology, Choquet integral, 0502 economics and business, [INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB], Conviction, 050207 economics, business, Social psychology, Social influence
Abstract: A group of agents is faced with a collective decisional problem. The corresponding debate is viewed as a dynamic process. An initial theoretical model based on a multicriteria decision framework was proposed in [7], yet without semantic justifications and any explicit dynamic representation. A second descriptive model has been proposed in [6], according to which social influences and argumentation strategy govern the dynamics of the debate. This paper seeks to justify the equations introduced in [7], using the semantic concepts discussed in [6], in order to provide a model for a debate held within the framework of control theory that explicitly lays out the dynamic aspects and moreover offers additional perspectives for purposes of controlling the debate
Published: 2013

50. An Envisioned Approach for Modeling and Supporting User-Centric Query Activities on Data Warehouses

Author: Marie-Aude Aufaure, Rokia Missaoui, Alfredo Cuzzocrea, Cécile Favre, Patrick Marcel, M. A., Aufaure, Cuzzocrea, Alfredo Massimiliano, C., Favre, P., Marcel, R., Missaoui, SID, Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2)-Université Lumière - Lyon 2 (UL2), Université du Québec en Outaouais (UQO), and Dagstuhl Seminar 11361
Subjects: Multidimensional Data, Knowledge representation and reasoning, User-Centric Data Warehousing, Computer science, Interface (computing), 02 engineering and technology, Knowledge Management Principles, Ontology (information science), Data Warehouses, Knowledge extraction, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, User-Centric, Metadata, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], Information retrieval, User-Centric Architecture, Online analytical processing, Knowledge economy, InformationSystems_DATABASEMANAGEMENT, Cooperative OLAP Query Answering, Data Warehouses, Knowledge Management Principles, Metadata, Multidimensional Data, User-Centric, User-Centric Architecture, User-Centric Data Warehousing, Data warehouse, Hardware and Architecture, Cooperative OLAP Query Answering, 020201 artificial intelligence & image processing, Software
Abstract: In this vision paper, the authors discuss models and techniques for integrating, processing and querying data, information and knowledge within data warehouses in a user-centric manner. The user-centric emphasis allows us to achieve a number of clear advantages with respect to classical data warehouse architectures, whose most relevant ones are the following: (i) a unified and meaningful representation of multidimensional data and knowledge patterns throughout the data warehouse layers (i.e., loading, storage, metadata, etc); (ii) advanced query mechanisms and guidance that are capable of extracting targeted information and knowledge by means of innovative information retrieval and data mining techniques. Following this main framework, the authors first outline the importance of knowledge representation and management in data warehouses, where knowledge is expressed by existing ontology or patterns discovered from data. Then, the authors propose a user-centric architecture for OLAP query processing, which is the typical applicative interface to data warehouse systems. Finally, the authors propose insights towards cooperative query answering that make use of knowledge management principles and exploit the peculiarities of data warehouses (e.g., multidimensionality, multi-resolution, and so forth).
Published: 2013
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