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

1. 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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2. 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

3. Graded cubes of opposition and possibility theory with fuzzy events

Author

Henri Prade, Didier Dubois, Agnès Rico, 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 - 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), and Institut National Polytechnique de Toulouse - INPT (FRANCE)

Subjects
Pure mathematics, Cube of opposition, Opposition (planets), Square of opposition, 02 engineering and technology, 01 natural sciences, Fuzzy logic, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Theoretical Computer Science, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Fuzzy event, Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, 0101 mathematics, Mathematics, Possibility theory, Logique en informatique, Applied Mathematics, 010102 general mathematics, [INFO.INFO-LO]Computer Science [cs]/Logic in Computer Science [cs.LO], Informatique et langage, Intelligence artificielle, 16. Peace & justice, Apprentissage, Algebra, Philosophical logic, Set function, 020201 artificial intelligence & image processing, Cube, Software
Abstract

International audience; The paper discusses graded extensions of the cube of opposition, a structure that naturally emerges from the square of opposition in philosophical logic. These extensions of the cube of opposition agree with possibility theory and its four set functions. This extended cube then provides a synthetic and unified view of possibility theory. This is an opportunity to revisit basic notions of possibility theory, in particular regarding the handling of fuzzy events. It turns out that in possibility theory, two extensions of the four basic set functions to fuzzy events exist, which are needed for serving different purposes. The expressions of these extensions involve many-valued conjunction and implication operators that are related either via semi-duality or via residuation.

Published
2017
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4. 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

5. 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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6. 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

7. Towards a Robust Imprecise Linear Deconvolution

Author

Olivier Strauss, Agnès Rico, Image & Interaction (ICAR), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Equipe de Recherche en Ingénierie des Connaissances (ERIC), and Université Lumière - Lyon 2 (UL2)

Subjects
Blind deconvolution, Mathematical optimization, 010102 general mathematics, 02 engineering and technology, robustness, Inverse problem, deconvolution, 01 natural sciences, Interval valued, Choquet capacities, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Robustness (computer science), 0202 electrical engineering, electronic engineering, information engineering, inverse problem, 020201 artificial intelligence & image processing, Deconvolution, 0101 mathematics, Algorithm, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Impulse response, Mathematics
Abstract

International audience; Deconvolution consists of reconstructing a signal from blurred (and usually noisy) sensory observations. It requires perfect knowledge of the impulse response of the sensor. Relevant works in the litterature propose methods with improved precision and robustness. But those methods are not able to account for a partial knowledge of the impulse response of the sensor. In this article, we experimentally show that inverting a Choquet capacity-based model of an imprecise knowledge of this impulse response allows to robustly recover the measured signal. The method we use is an interval valued extension of the well known Schultz procedure.

Published
2013
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8. Towards interval-based non-additive deconvolution in signal processing

Author

Olivier Strauss, Agnès Rico, Image & Interaction (ICAR), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Equipe de Recherche en Ingénierie des Connaissances (ERIC), and Université Lumière - Lyon 2 (UL2)

Subjects
Blind deconvolution, Mathematical optimization, Finite impulse response, 02 engineering and technology, Deconvolution, Impulse (physics), 01 natural sciences, Theoretical Computer Science, 010104 statistics & probability, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0202 electrical engineering, electronic engineering, information engineering, 0101 mathematics, Impulse response, Non-additive confidence measure, Mathematics, Signal processing, Schultz iterative procedure, Inverse problem, 020201 artificial intelligence & image processing, Choquet integral, Geometry and Topology, Algorithm, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Software, Linear filter
Abstract

International audience; Reconstructing a signal from its observations via a sensor device is usually called "deconvolution". Such reconstruction requires perfect knowledge of the impulse response of the sensor involved in the signal measurement. The lower this knowledge, the more biased the reconstruction. In this paper, we present a novel method for reconstructing a signal measured by a sensor whose impulse response is imprecisely known. This technique is based on modeling the relationship between the measurement and the signal via a concave capacity and extending the convolution concept to a concave set of impulse responses. The reconstructed signal is interval-valued, thus reflecting the poor knowledge of the sensor impulse response.

Published
2012
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9. Du cadastre ancien au graphe. Les dynamiques spatiales dans les sources fiscales médiévales et modernes

Author

Mélanie Le Couédic, Bertrand Jouve, Samuel Leturcq, Xavier Rodier, Florent Hautefeuille, Etienne Fieux, Cités, Territoires, Environnement et Sociétés (CITERES), Centre National de la Recherche Scientifique (CNRS)-Université de Tours (UT), Travaux et recherches archéologiques sur les cultures, les espaces et les sociétés (TRACES), 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), Institut de Mathématiques de Toulouse UMR5219 (IMT), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-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), Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2), ANR-09-BLAN-0322,MODELESPACE,Modélisation spatiale des sources du prélèvement foncier préindustriel : textes, graphes, SIG(2009), Centre National de la Recherche Scientifique (CNRS)-Université de Tours, École des hautes études en sciences sociales (EHESS)-Université Toulouse - Jean Jaurès (UT2J)-Ministère de la Culture et de la Communication (MCC)-Centre National de la Recherche Scientifique (CNRS), France, Amériques, Espagne – Sociétés, pouvoirs, acteurs (FRAMESPA), Université Toulouse - Jean Jaurès (UT2J)-Centre National de la Recherche Scientifique (CNRS), Université de Tours (UT)-Centre National de la Recherche Scientifique (CNRS), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), École des hautes études en sciences sociales (EHESS)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Ministère de la Culture et de la Communication (MCC)-Centre National de la Recherche Scientifique (CNRS), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), and Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Archeology, cadaster, [SHS.ARCHEO]Humanities and Social Sciences/Archaeology and Prehistory, 010102 general mathematics, terrier, Geographic Information Systems (GIS), 0102 computer and information sciences, [SHS.GEO]Humanities and Social Sciences/Geography, cadastre, SIG, GIS, 01 natural sciences, Graph theory, 010201 computation theory & mathematics, Political science, Medieval Ages, système d’information géographique (SIG), système d'information géographique (SIG), théorie des graphes, fieldbook, Modern period, 0101 mathematics, [SHS.HIST]Humanities and Social Sciences/History, période moderne, Humanities
Abstract

http://archeosciences.revues.org/3758; International audience; Medieval and modern tax documents (fieldbooks, “compoix”, cadasters...) give a rich spatial information. Whole territories are described plot per plot at different succesive periods. Nevertheless, historians don’t know how to relate different states of spatial information on the long term. Moreover, there are no plot plans before the 17th and 18th century. We want to overcome this methodological lock with the following proposition: modelling ancient plots described in tax documents using the topological proprieties of the graph theory. The translation of the spatial data into graphs should allow to set up a commun language between the historical documents, mapped and not mapped. The article talks about the fondamental method choices and the used process to transform the spatial information of ancient plans into graphs which will allow us not only to compare them together, but also to compare them with cadastral registers that have no plan. Finally, it is within a Geographic Information System that will be tested the pertinence of comparing possibilities while studying the plot changes according to the spatiotemporal operators of changement (creation, disappearance, stability, dilatation, contraction, fusion, fission, deformation).; Les documents fiscaux médiévaux et modernes (terriers, compoix, cadastres…) offrent une information spatiale riche. Des territoires entiers sont décrits parcelle par parcelle à des époques successives. Les historiens ne parviennent toutefois jamais à corréler ces différents états de l’information spatiale sur la longue durée, d’autant qu’on ne dispose d’aucun plan parcellaire avant les xviie-xviiie siècles. C’est ce verrou méthodologique qu’il s’agit de dépasser en proposant, dans le cadre de l’ANR Modelespace, une modélisation des parcellaires anciens décrits dans les documents fiscaux en mettant en œuvre les propriétés topologiques de la théorie des graphes. La conversion des données spatiales en graphes doit permettre de mettre en place un langage commun entre les documents historiques. L’article traite des choix méthodologiques fondamentaux et de la démarche utilisée pour transformer l’information spatiale des plans anciens en graphes susceptibles d’être appariés non seulement entre eux, mais encore avec des graphes issus de matrices cadastrales sans plan. Au final, c’est au sein d’un Système d’Information Géographique que devra être testée la pertinence des possibilités de comparaisons en étudiant les recompositions parcellaires selon des opérateurs spatio-temporels de changement (création, disparition, stabilité, dilatation, contraction, fusion, fission, déformation).

Published
2012
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10. Middle Age Social Networks: A Dynamic Organizational Study

Author

Florent Hautefeuille, Yves Demazeau, Valérie Camps, Bertrand Jouve, Laurent Lacomme, Laboratoire d'Informatique de Grenoble (LIG), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Systèmes Multi-Agents Coopératifs (IRIT-SMAC), 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), Université Toulouse III - Paul Sabatier (UT3), Modélisation d’agents autonomes en univers multi-agents (MAGMA), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Travaux et recherches archéologiques sur les cultures, les espaces et les sociétés (TRACES), École des hautes études en sciences sociales (EHESS)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Ministère de la Culture et de la Communication (MCC)-Centre National de la Recherche Scientifique (CNRS), Institut de Mathématiques de Toulouse UMR5219 (IMT), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), 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, 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), Equipe de Recherche en Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2), MAGMA, Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse 1 Capitole (UT1), and Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)

Subjects
Quercy, Moyen Age, Computer science, paysan, 01 natural sciences, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 010104 statistics & probability, Order (exchange), graphes, 0103 physical sciences, Global network, 0101 mathematics, ComputingMilieux_MISCELLANEOUS, Structure (mathematical logic), Social network, Markov chain, Point (typography), 010308 nuclear & particles physics, business.industry, réseau social, Data science, socialibilité, Dynamics (music), [INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA], Artificial intelligence, business, [SHS.HIST]Humanities and Social Sciences/History
Abstract

This article describes the main components of the multiagent model and the tool we are designing in order to analyze the structure and the dynamics of an historical social network from the Middle Age. This social network has been built through the discovery of many notarized documents and first studied according to a mathematical approach. Using a customizable multiagent system to simulate the network’s evolution and Markov chain calculus to adjust behavioral parameters, we propose a method to assess behaviors’ importance and effects on the global network’s evolution. From a historical point of view, this multiagent model also aims at explaining this network’s dynamics during some specific periods such as the Hundred Year’s War where numerous notarized documents disappeared.

Published
2011
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11. Graphical methods for investigating the finite-sample properties of confidence regions

Author

Christian de Peretti, Carole Siani, Laboratoire de Sciences Actuarielle et Financière (SAF), 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), and Rico, Fabien

Subjects
Statistics and Probability, [STAT.ME] Statistics [stat]/Methodology [stat.ME], Applied Mathematics, Numerical analysis, 05 social sciences, Context (language use), Sample (statistics), Graphical display, 01 natural sciences, Confidence interval, 010104 statistics & probability, Computational Mathematics, Computational Theory and Mathematics, Autoregressive model, 0502 economics and business, Statistics, 0101 mathematics, Spurious relationship, [STAT.ME]Statistics [stat]/Methodology [stat.ME], 050205 econometrics, Confidence region, Mathematics
Abstract

CSDA09PS; International audience; No satisfactory method for measuring and presenting the performance of confidence regions can be found in the literature. Techniques for measuring the effectiveness of confidence regions and for the graphical display of simulation evidence regarding the coverage and effectiveness of confidence regions are developed and illustrated. Three types of figures are discussed: called coverage plots, coverage discrepancy plots, and coverage effectiveness curves, which makes it possible to show the ''genuine'' effectiveness, rather than a spurious nominal effectiveness. These figures are used to illustrate the finite sample properties of autoregressive parameter confidence regions in the context of AR(1) processes.

Published
2010
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12. On the behavior of the generalizations of the intensity of implication: A data-driven comparative study

Author

Stéphane Lallich, Benoît Vaillant, Philippe Lenca, Institut Universiteire de Technologie de Vannes (UBS - Université de Bretagne Sud) (IUT Vannes), 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), and Studies in computational intelligence

Subjects
021103 operations research, 0211 other engineering and technologies, 02 engineering and technology, 16. Peace & justice, 01 natural sciences, Data-driven, Intensity of implication, 010104 statistics & probability, [INFO.INFO-CY]Computer Science [cs]/Computers and Society [cs.CY], Statistical interestingness measure, Econometrics, 0101 mathematics, Generalized measures, Intensity (heat transfer), Mathematics
Abstract

International audience; In this chapter, we present an original and synthetical overview of most of the commonly used association rule statistical interestingness measures introduced in previous works. These measures usually relate the confidence of a rule to an independency reference situation. Others relate it to indetermination, or impose a minimum confidence threshold. We propose a systematic generalization of these measures, taking into account a reference point, chosen by an expert, in order to apprehend the confidence of a rule. This generalization introduces new connections between measures. They lead to the enhancement of some measures. We then propose new parameterized possibilities. The behavior of the parameterized measures is illustrated using classical datasets, and these measures are compared to their original counter-parts. This study highlights the different properties of each of them and discusses the advantages of our proposition.

Published
2008
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13. A test to control a region growing process within a hierarchical graph

Author

Jean-Michel Jolion, Stéphane Lallich, Fabrice Muhlenbach, 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), Université de Lyon-Université Lumière - Lyon 2 (UL2), and SI LIRIS, Équipe gestionnaire des publications

Subjects
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Scale-space segmentation, 02 engineering and technology, [INFO] Computer Science [cs], 01 natural sciences, Composite image filter, 010104 statistics & probability, Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, Segmentation, [INFO]Computer Science [cs], 0101 mathematics, Mathematics, Iterative and incremental development, Decimation, business.industry, Segmentation-based object categorization, Pattern recognition, Image segmentation, Region growing, Computer Science::Computer Vision and Pattern Recognition, Signal Processing, 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Artificial intelligence, business, Software
Abstract

International audience; Hierarchical graphs using a decimation process can be applied to animage to obtain segmentation. However, this method encounters limitations.First the decimation proceeds at each step in a general way on theentire image. Second, a criterion is needed to stop the iterative process whenthe best segmentation is obtained. We propose a statistical testto control the region growing process. This test, based on Moran's spatialautocorrelation coefficient, controls the decimation process both globally andlocally at the same time. Results on a composite image indicate that the localand global tests are a powerful tool for optimizing the image segmentationprocess.

Published
2003

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