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81 results on '"Hullermeier, Eyke"'

1. On the Bayes-optimality of F-measure maximizers

Author

Waegeman, Willem, Dembczynski, Krzysztof, Jachnik, Arkadiusz, Cheng, Weiwei, and Hullermeier, Eyke

Subjects
Statistics - Machine Learning, Computer Science - Learning
Abstract

The F-measure, which has originally been introduced in information retrieval, is nowadays routinely used as a performance metric for problems such as binary classification, multi-label classification, and structured output prediction. Optimizing this measure is a statistically and computationally challenging problem, since no closed-form solution exists. Adopting a decision-theoretic perspective, this article provides a formal and experimental analysis of different approaches for maximizing the F-measure. We start with a Bayes-risk analysis of related loss functions, such as Hamming loss and subset zero-one loss, showing that optimizing such losses as a surrogate of the F-measure leads to a high worst-case regret. Subsequently, we perform a similar type of analysis for F-measure maximizing algorithms, showing that such algorithms are approximate, while relying on additional assumptions regarding the statistical distribution of the binary response variables. Furthermore, we present a new algorithm which is not only computationally efficient but also Bayes-optimal, regardless of the underlying distribution. To this end, the algorithm requires only a quadratic (with respect to the number of binary responses) number of parameters of the joint distribution. We illustrate the practical performance of all analyzed methods by means of experiments with multi-label classification problems.

Published
2013

3. Actes de la conférence CAID 2020

Author

de Vieilleville, François, May, Stéphane, Lagrange, Adrien, Dupuis, A, Ruiloba, Rosa, Ngolè Mboula, Fred, Bitard-Feildel, Tristan, Nogues, Erwan, Larroche, Corentin, Mazel, Johan, Clémençon, Stephan, Burgot, Romain, Gaurier, Alric, Hulot, Louis, Isaac-Dognin, Léo, Leichtnam, Laetitia, Totel, Eric, Prigent, Nicolas, Mé, Ludovic, Bernhard, Rémi, Moëllic, Pierre-Alain, Dutertre, Jean-Max, Kapusta, Katarzyna, Thouvenot, Vincent, Bettan, Olivier, Charrier, Tristan, Bonnafoux, Luc, Puig, Francisco-Pierre, Lhoest, Quentin, Renault, Thomas, Benamira, Adrien, Bonnet, Benoit, Furon, Teddy, Bas, Patrick, Farcy, Benjamin, Gil-Casals, Silvia, Mattioli, Juliette, Fiammante, Marc, Lambert, Marc, Bresson, Roman, Cohen, Johanne, Hullermeier, Eyke, Labreuche, Christophe, Sebag, Michele, Thebaud, Thomas, Larcher, Anthony, Le Lan, Gaël, Nour, Nouredine, Belhaj-Soullami, Reda, Buron, Cédric, Peres, Alain, Barbaresco, Frédéric, D’acremont, Antoine, Quin, Guillaume, Baussard, Alexandre, Fablet, Ronan, Corbineau, Marie-Caroline, Morge-Rollet, Louis, Le Roy, Frederic, Le Jeune, Denis, Gautier, Roland, Camus, Benjamin, Monteux, Eric, Vermet, Mikaël, Goupilleau, Alex, Ceillier, Tugdual, Agenium Space [Toulouse], Centre National d'Études Spatiales [Toulouse] (CNES), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), DGA Maîtrise de l'information (DGA.MI), Direction générale de l'Armement (DGA), Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES), Agence nationale de la sécurité des systèmes d'information (ANSSI), Département Images, Données, Signal (IDS), Télécom ParisTech, Signal, Statistique et Apprentissage (S2A), Laboratoire Traitement et Communication de l'Information (LTCI), Institut Mines-Télécom [Paris] (IMT)-Télécom Paris-Institut Mines-Télécom [Paris] (IMT)-Télécom Paris, Institut Polytechnique de Paris (IP Paris), Capgemini Consulting [Paris], CentraleSupélec, Département Systèmes Réseaux, Cybersécurité et Droit du numérique (IMT Atlantique - SRCD), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), LSTI, Confidentialité, Intégrité, Disponibilité et Répartition (CIDRE), CentraleSupélec-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SYSTÈMES LARGE ÉCHELLE (IRISA-D1), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-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)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes 1 (UR1), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), École des Mines de Saint-Étienne (Mines Saint-Étienne MSE), Institut Mines-Télécom [Paris] (IMT), Thales SIX GTS France, THALES COMMUNICATIONS & SECURITY, THALES, Centre National de la Recherche Scientifique (CNRS), Creating and exploiting explicit links between multimedia fragments (LinkMedia), MEDIA ET INTERACTIONS (IRISA-D6), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria), Centrale Lille, Thales (France), IBM France Lab [Biot], IBM - Paris [Bois-Colombes], IBM-IBM, Délégation générale de l'armement (DGA), Ministère de la Défense, Thales Research and Technology [Palaiseau], University of Paderborn, TAckling the Underspecified (TAU), 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)-Laboratoire Interdisciplinaire des Sciences du Numérique (LISN), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Informatique de l'Université du Mans (LIUM), Le Mans Université (UM), Orange Labs [Cesson-Sévigné], Orange Labs, NukkAI [Paris], École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne), MBDA France [Le Plessis Robinson], 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)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Université de Brest (UBO), Scalian [Rennes], Earthcube [Paris], DGA, DGNUM, Ministère des Armées, Université de Bretagne Sud (UBS)-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)-Université de Rennes (UNIV-RENNES)-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 Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-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)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-MEDIA ET INTERACTIONS (IRISA-D6), Institut National des Sciences Appliquées (INSA), IMT Atlantique (IMT Atlantique), 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)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-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)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), THALES [France], Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Equipe Security, Intelligence and Integrity of Information (Lab-STICC_SI3), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-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 Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), and Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-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 Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique)

Subjects
systèmes embarqués, intelligence artificielle, cyber-sécurité, prise de décision, signaux radar, artificial intelligence, sécurité des données, ComputingMilieux_MISCELLANEOUS, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

National audience

Published
2021

4. Explanation as a Social Practice: Toward a Conceptual Framework for the Social Design of AI Systems

Author

Rohlfing, Katharina J., primary, Cimiano, Philipp, additional, Scharlau, Ingrid, additional, Matzner, Tobias, additional, Buhl, Heike M., additional, Buschmeier, Hendrik, additional, Esposito, Elena, additional, Grimminger, Angela, additional, Hammer, Barbara, additional, Hab-Umbach, Reinhold, additional, Horwath, Ilona, additional, Hullermeier, Eyke, additional, Kern, Friederike, additional, Kopp, Stefan, additional, Thommes, Kirsten, additional, Ngonga Ngomo, Axel-Cyrille, additional, Schulte, Carsten, additional, Wachsmuth, Henning, additional, Wagner, Petra, additional, and Wrede, Britta, additional

Published
2021
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10. In defense of fuzzy association analysis

Author

Hullermeier, Eyke and Yi, Yu

Subjects
Data warehousing/data mining, Fuzzy logic, Data mining -- Analysis, Fuzzy algorithms -- Analysis, Fuzzy logic -- Analysis, Fuzzy systems -- Analysis
Abstract

This short correspondence is a reply to a recently published paper by Verlinde et al. in which the authors empirically compared fuzzy and nonfuzzy association analysis and, on the basis of their results, questioned the usefulness of a fuzzy approach. Although we highly welcome the critical examination of the topic and definitely agree that fuzzy extensions of existing methods call for a thorough justification, the empirical comparison presented in the aforementioned paper is in our opinion not objective and extensive enough to fully warrant the conclusions drawn from the results. Apart from some general comments on the claims raised in their paper, we present empirical results based on an alternative experimental setup that lead to different conclusions. Index Terms--Association rules, data mining, fuzzy sets, partitioning.

Published
2007

11. Credible case-based inference using similarity profiles

Author

Hullermeier, Eyke

Subjects
Problem solving -- Methods, Reasoning -- Usage, Business, Computers, Electronics, Electronics and electrical industries
Abstract

In this paper, we propose a method for retrieving promising candidate solutions in case-based problem solving. Our method, referred to as credible case-based inference, makes use of so-called similarity profiles as a formal model of the key hypothesis underlying case-based reasoning (CBR), namely, the assumption that similar problems have similar solutions. Proceeding from this formalization, it becomes possible to derive theoretical properties of the corresponding inference scheme in a rigorous way. In particular, it can be shown that, under mild technical conditions, a set of candidates covers the true solution with high probability. Thus, the approach supports an important subtask in CBR, namely, to generate potential solutions for a new target problem in a sound manner and hence contributes to the methodical foundations of CBR. Due to its generality, it can be employed for different types of performance tasks and can easily be integrated in existing CBR systems. Index Terms--Case-based reasoning, prediction, instance-based learning.

Published
2007

12. Experience-based decision making: a satisficing decision tree approach

Author

Hullermeier, Eyke

Subjects
Machine learning -- Research, Decision-making -- Research
Abstract

This paper introduces a framework of experienced-based decision making as an extension of case-based decision making, a recently proposed alternative to expected utility theory. In experienced-based decision making, an agent faced with a new decision problem acts on the basis of experience gathered from previous problems in the past, either through predicting the utility of potential actions or through establishing a direct relationship between decision problems and appropriate actions. In the paper, a realization of the latter approach in the form of "satisficing" decision trees is proposed. Index Terms--Bounded rationality, case-based reasoning, compilation, decision making, decision trees, machine learning.

Published
2005

16. Research Directions for Principles of Data Management

Author

Abiteboul, Serge, Arenas, Marcelo, Barceló, Pablo, Bienvenu, Meghyn, Calvanese, Diego, David, Claire, Hull, Richard, Hullermeier, Eyke, Kimelfeld, Benny, Libkin, Leonid, Martens, Wim, Milo, Tova, Murlak, Filip, Neven, Frank, Ortiz, Magdalena, Schwentick, Thomas, Stoyanovich, Julia, Su, Jianwen, Suciu, Dan, Vianu, Victor, Yi, Ke, Laboratoire Spécification et Vérification [Cachan] (LSV), École normale supérieure - Cachan (ENS Cachan)-Centre National de la Recherche Scientifique (CNRS), Pontificia Universidad Católica de Chile (UC), Universidad de Chile = University of Chile [Santiago] (UCHILE), Université de Montpellier (UM), Free University of Bozen-Bolzano, Laboratoire d'Informatique Gaspard-Monge (LIGM), Centre National de la Recherche Scientifique (CNRS)-Fédération de Recherche Bézout-ESIEE Paris-École des Ponts ParisTech (ENPC)-Université Paris-Est Marne-la-Vallée (UPEM), IBM Thomas J. Watson Research Center, IBM, Universität Paderborn (UPB), Technion - Israel Institute of Technology [Haifa], Laboratory for the Foundations of Computer Science [Edinburgh] (LFCS), University of Edinburgh, Universität Bayreuth, School of Computer Science (TAU-CS), Tel Aviv University [Tel Aviv], Institute of Informatics [Warsaw], Faculty of Mathematics, Informatics, and Mechanics [Warsaw] (MIMUW), University of Warsaw (UW)-University of Warsaw (UW), School for Information Technology, Transnational University of Limburg-Hasselt University (UHasselt), Vienna University of Technology (TU Wien), Lehrstuhl Informatik 1 [Dortmund], Technische Universität Dortmund [Dortmund] (TU), Drexel University, Department of Computer Science [Santa Barbara] (CS-UCSB), University of California [Santa Barbara] (UCSB), University of California-University of California, University of Washington [Seattle], Department of Computer Science and Engineering [San Diego] (CSE-UCSD), University of California [San Diego] (UC San Diego), Department of Computer Science and Engineering [HKUST] (CSE), and Hong Kong University of Science and Technology (HKUST)

Subjects
query languages, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], database theory, efficient query processing, heterogeneous data, principles of data management, uncertainty, query optimization
Abstract

International audience; The area of Principles of Data Management (PDM) has made crucial contributions to the development of formal frameworks for understanding and managing data and knowledge. This work has involved a rich cross-fertilization between PDM and other disciplines in mathematics and computer science, including logic, complexity theory, and knowledge representation. We anticipate ongoing expansion of PDM research as the technology and applications involving data management continue to grow and evolve. In particular, the lifecycle of Big Data Analytics raises a wealth of challenge areas that PDM can help with. In this report we identify some of the most important research directions where the PDM community has the potential to make significant contributions. This is done from three perspectives: potential practical relevance, results already obtained, and research questions that appear surmountable in the short and medium term.

Published
2018

18. Fuzzy Sets and Systems : Special Issue Celebrating the 50th Anniversary of Fuzzy Sets (2015)

Author

De Baets, Bernard, Dubois, Didier, Hullermeier, Eyke, De Baets, Bernard, Dubois, Didier, Hullermeier, Eyke, Hogeschool Gent, 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), and University of Paderborn

Subjects
[INFO]Computer Science [cs]
Abstract

International audience; This special issue celebrates the publication of the first paper on fuzzy sets by its founder Lotfi Zadeh in 1965.1 Without any doubt, this paper is the most important landmark for the research field in general and for Fuzzy Sets and Systems in particular. In fact, the mere existence of the oldest scientific journal on the topic is only due to Zadeh's publication. On this occasion, we invited a number of esteemed colleagues, many of whom are serving the journal as members of the editorial board, to deliver their thoughts on their topic of predilection and competence. We thus managed to collect twenty position papers that altogether provide a relatively exhaustive overview of the current activity on fuzzy sets and their applications, and of questions regarding the role and appropriateness of fuzzy sets in various topics. Thus, going beyond a pure celebration of the past, this issue is also meant to indicate promising directions of future research on fuzzy sets. A tout seigneur tout honneur,2 we are extremely lucky to publish one more paper by the author of the publication we celebrate. Lotfi Zadeh himself provides his own testimony about the genesis of his first paper on fuzzy sets, and he also expresses his opinions concerning new directions to be explored in the future. In a second contribution, Dubois and Prade provide a glimpse at the various areas of research that were influenced by fuzzy sets, pointing out mature fields, barren directions, and promising areas of research. One of their main points is the necessity of interpreting membership functions in applied research. It is important to articulate the contribution of fuzzy sets in the various problems they are applied to. This question is crucial to explain where degrees of membership come from and how they can be measured. The answer will be different if membership functions encode ideas of similarity, of uncertainty or yet of preference. The rest of the issue is clustered into various themes: logic, mathematics, systems engineering, decision and optimization, data analysis and management.

Published
2015

24. Representing, Processing, and Learning Preferences : Theoretical and Practical Challenges

Author

Domshlak, Carmel, Hullermeier, Eyke, Kaci, Souhila, Prade, Henri, Technion - Israel Institute of Technology [Haifa], Philipps Universität Marburg, Centre de Recherche en Informatique de Lens (CRIL), Université d'Artois (UA)-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, Domshlak, Carmel, Hullermeier, Eyke, Kaci, Souhila, Prade, Henri, and Grélaud, Françoise

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Preference queries, Graphical representation, Logical representation, ComputingMilieux_MISCELLANEOUS, Aggregation functions, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience

Published
2011

29. A Fuzzy Variant of the Rand Index for Comparing Clustering Structures

Author

Hullermeier, Eyke, Rifqi, Maria, Computational Intelligence Group, Fachbereich Mathematik und Informatik [Marburg] [Dept. of Math and Computer Science], Philipps Universität Marburg-Philipps Universität Marburg, Machine Learning and Information Retrieval (MALIRE), Laboratoire d'Informatique de Paris 6 (LIP6), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Rifqi, Maria, and Philipps Universität Marburg = Philipps University of Marburg -Philipps Universität Marburg = Philipps University of Marburg

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], ComputingMethodologies_PATTERNRECOGNITION, Mathematics::General Mathematics, ComputingMethodologies_GENERAL, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience; In this paper, we introduce a fuzzy extension of the Rand index, a well-known measure for comparing two clustering structures. In contrast to an existing proposal, which is restricted to the comparison of a fuzzy partition with a non-fuzzy reference partition, our extension is able to compare two proper fuzzy partitions with each other. Elaborating on the formal properties of our fuzzy Rand index, we show that it exhibits desirable metrical properties.

Published
2009

31. Instance-based prediction in the framework of possibility theory

Author

Hullermeier, Eyke, Dubois, Didier, Prade, Henri, Technische Universität Dortmund [Dortmund] (TU), 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, and Grélaud, Françoise

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience; A possibilistic framework for instance-based prediction is presented which formalizes the generalization beyond experience by means of fuzzy rules. In comparison with related instance-based approaches such as the well-known Nearest Neighbor classifier, this method distinguishes itself by the following: First, by suggesting (guaranteed) degrees of possibility for competing outcomes rather than making precise predictions, it takes the uncertain character of similarity-based inference into account. Second, the possibilistic framework can easily be extended so as to cope with incompletely specified cases. Thirdly, the close connection between possibility theory and fuzzy sets suggests the extension of the basic model by means of fuzzy set-based (linguistic) modeling techniques. This paper especially highlights two of these aspects, namely the modeling of uncertainty and the handling of incomplete information.

Published
2001

47. Preference Learning Using the Choquet Integral: The Case of Multipartite Ranking.

Author

Tehrani, Ali Fallah, Cheng, Weiwei, and Hullermeier, Eyke

Subjects
DECISION theory, DECISION making, MULTIPLE criteria decision making, ARTIFICIAL intelligence, RANKING (Statistics), MANAGEMENT science
Abstract

We propose a novel method for preference learning or, more specifically, learning to rank, where the task is to learn a ranking model that takes a subset of alternatives as input and produces a ranking of these alternatives as output. Just like in the case of conventional classifier learning, training information is provided in the form of a set of labeled instances, with labels or, say, preference degrees taken from an ordered categorical scale. This setting is known as multipartite ranking in the literature. Our approach is based on the idea of using the (discrete) Choquet integral as an underlying model for representing ranking functions. Being an established aggregation function in fields such as multiple criteria decision making and information fusion, the Choquet integral offers a number of interesting properties that make it attractive from a machine learning perspective, too. The learning problem itself comes down to properly specifying the fuzzy measure on which the Choquet integral is defined. This problem is formalized as a margin maximization problem and solved by means of a cutting plane algorithm. The performance of our method is tested on a number of benchmark datasets. [ABSTRACT FROM PUBLISHER]

Published
2012
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48. Superposition and Alignment of Labeled Point Clouds.

Author

Fober, Thomas, Glinca, Gerghei, Klebe, Gerhard, and Hullermeier, Eyke

Abstract

Geometric objects are often represented approximately in terms of a finite set of points in three-dimensional euclidean space. In this paper, we extend this representation to what we call labeled point clouds. A labeled point cloud is a finite set of points, where each point is not only associated with a position in three-dimensional space, but also with a discrete class label that represents a specific property. This type of model is especially suitable for modeling biomolecules such as proteins and protein binding sites, where a label may represent an atom type or a physico-chemical property. Proceeding from this representation, we address the question of how to compare two labeled points clouds in terms of their similarity. Using fuzzy modeling techniques, we develop a suitable similarity measure as well as an efficient evolutionary algorithm to compute it. Moreover, we consider the problem of establishing an alignment of the structures in the sense of a one-to-one correspondence between their basic constituents. From a biological point of view, alignments of this kind are of great interest, since mutually corresponding molecular constituents offer important information about evolution and heredity, and can also serve as a means to explain a degree of similarity. In this paper, we therefore develop a method for computing pairwise or multiple alignments of labeled point clouds. To this end, we proceed from an optimal superposition of the corresponding point clouds and construct an alignment which is as much as possible in agreement with the neighborhood structure established by this superposition. We apply our methods to the structural analysis of protein binding sites. [ABSTRACT FROM PUBLISHER]

Published
2011
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49. SEGA: Semiglobal Graph Alignment for Structure-Based Protein Comparison.

Author

Mernberger, Marco, Klebe, Gerhard, and Hullermeier, Eyke

Abstract

Comparative analysis is a topic of utmost importance in structural bioinformatics. Recently, a structural counterpart to sequence alignment, called multiple graph alignment, was introduced as a tool for the comparison of protein structures in general and protein binding sites in particular. Using approximate graph matching techniques, this method enables the identification of approximately conserved patterns in functionally related structures. In this paper, we introduce a new method for computing graph alignments motivated by two problems of the original approach, a conceptual and a computational one. First, the existing approach is of limited usefulness for structures that only share common substructures. Second, the goal to find a globally optimal alignment leads to an optimization problem that is computationally intractable. To overcome these disadvantages, we propose a semiglobal approach to graph alignment in analogy to semiglobal sequence alignment that combines the advantages of local and global graph matching. [ABSTRACT FROM PUBLISHER]

Published
2011
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50. Reliability Maps:A Tool to Enhance Probability Estimates and Improve Classification Accuracy (Best paper award)

Author

Kull, Meelis, Flach, Peter A., Calders, Toon, Esposito, Floriana, Hullermeier, Eyke, and Meo, Rosa

Subjects
Jean Golding
Abstract

We propose a general method to assess the reliability of two-class probabilities in an instance-wise manner. This is relevant, for instance, for obtaining calibrated multi-class probabilities from two-class probability scores. The LS-ECOC method approaches this by performing least-squares fitting over a suitable error-correcting output code matrix, where the optimisation resolves potential conflicts in the input probabilities. While this gives all input probabilities equal weight, we would like to spend less effort fitting unreliable probability estimates. We introduce the concept of a reliability map to accompany the more conventional notion of calibration map; and LS-ECOC-R which modifies LS-ECOC to take reliability into account. We demonstrate on synthetic data that this gets us closer to the Bayes-optimal classifier, even if the base classifiers are linear and hence have high bias. Results on UCI data sets demonstrate that multi-class accuracy also improves. © 2014 Springer-Verlag.

Published
2014

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