Embed Size (px)
Citation preview
Lecture Notes in Artificial Intelligence 6178Edited by R. Goebel, J. Siekmann, and W. Wahlster
Subseries of Lecture Notes in Computer Science
Eyke Hüllermeier Rudolf KruseFrank Hoffmann (Eds.)
Computational Intelligencefor Knowledge-BasedSystems Design
13th International Conferenceon Information Processing and Managementof Uncertainty, IPMU 2010Dortmund, Germany, June 28 - July 2, 2010Proceedings
13
Series Editors
Randy Goebel, University of Alberta, Edmonton, CanadaJörg Siekmann, University of Saarland, Saarbrücken, GermanyWolfgang Wahlster, DFKI and University of Saarland, Saarbrücken, Germany
Volume Editors
Eyke HüllermeierPhilipps-Universität Marburg, Fachbereich Mathematik und InformatikHans-Meerwein-Str., 35032 Marburg, GermanyE-mail: [email protected]
Rudolf KruseOtto-von-Guericke-Universität Magdeburg, Fakultät InformatikUniversitätsplatz 2, 39106 Magdeburg, GermanyE-mail: [email protected]
Frank HoffmannTechnische Universität DortmundFakultät für Elektrotechnik und InformationstechnikOtto-Hahn-Str. 4, 44227 Dortmund, GermanyE-mail: [email protected]
Library of Congress Control Number: 2010929051
CR Subject Classification (1998): I.2, H.3, F.1, H.4, I.5, I.4
LNCS Sublibrary: SL 7 – Artificial Intelligence
ISSN 0302-9743
ISBN-10 3-642-14048-3 Springer Berlin Heidelberg New YorkISBN-13 978-3-642-14048-8 Springer Berlin Heidelberg New York
This work is subject to copyright. All rights are reserved, whether the whole or part of the material isconcerned, specifically the rights of translation, reprinting, re-use of illustrations, recitation, broadcasting,reproduction on microfilms or in any other way, and storage in data banks. Duplication of this publicationor parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965,in its current version, and permission for use must always be obtained from Springer. Violations are liableto prosecution under the German Copyright Law.
springer.com
© Springer-Verlag Berlin Heidelberg 2010Printed in Germany
Typesetting: Camera-ready by author, data conversion by Scientific Publishing Services, Chennai, IndiaPrinted on acid-free paper 06/3180
Preface
The International Conference on Information Processing and Management ofUncertainty in Knowledge-Based Systems, IPMU, is organized every two yearswith the aim of bringing together scientists working on methods for the man-agement of uncertainty and aggregation of information in intelligent systems.Since 1986, this conference has been providing a forum for the exchange of ideasbetween theoreticians and practitioners working in these areas. The 13th IPMUconference took place in Dortmund, Germany, June 28–July 2, 2010.
This volume contains 77 papers selected through a rigorous reviewing processamong 320 submissions from 36 countries. The contributions reflect the richnessof research in the field of computational intelligence and represent several im-portant developments, specifically focused on the following subfields:
(a) machine learning, data mining, and pattern recognition,(b) uncertainty handling,(c) aggregation and fusion of information,(d) logic and knowledge processing.
We were delighted that Melanie Mitchell (Portland State University, USA),Nihkil R. Pal (Indian Statistical Institute), Bernhard Scholkopf (Max Planck Ins-titute for Biological Cybernetics, Tubingen, Germany) and Wolfgang Wahlster(German Research Center for Artificial Intelligence, Saarbrucken) accepted ourinvitations to present keynote lectures. Jim Bezdek received the Kampe de FerietAward, granted every two years on the occasion of the IPMU conference, in viewof his eminent research contributions to the handling of uncertainty in clustering,data analysis and pattern recognition.
Organizing a conference like this one is not possible without the assistanceand continuous support of many people and institutions. We are particularlygrateful to the organizers of sessions on dedicated topics that took place duringthe conference—these ‘special sessions’ have always been a characteristic ele-ment of the IPMU conference. Frank Klawonn and Thomas Runkler helped alot to evaluate and select special session proposals. The special session organizersthemselves rendered important assistance in the reviewing process, that was fur-thermore supported by the Area Chairs and regular members of the ProgrammeCommittee. Thomas Fober has been the backbone on several organizational andelectronic issues, and also helped with the preparation of the proceedings. In thisregard, we would also like to thank Alfred Hofmann and Springer for providingcontinuous assistance and ready advice whenever needed.
VI Preface
Finally, we gratefully acknowledge the support of several organizations andinstitutions, notably the German Informatics Society (Gesellschaft fur Infor-matik, GI), the German Research Foundation (DFG), the European Societyfor Fuzzy Logic and Technology (EUSFLAT), the International Fuzzy SystemsAssociation (IFSA), the North American Fuzzy Information Processing Society(NAFIPS) and the IEEE Computational Intelligence Society.
April 2010 Eyke HullermeierRudolf Kruse
Frank Hoffmann
Organization
Conference CommitteeGeneral Chair Eyke Hullermeier (Philipps-Universitat Marburg)Co-chairs Frank Hoffmann (Technische Universitat Dortmund)
Rudolf Kruse (Otto-von-Guericke Universitat Magdeburg)Frank Klawonn (Hochschule Braunschweig-Wolfenbuttel)Thomas Runkler (Siemens AG, Munchen)
Web Chair Thomas Fober (Philipps-Universitat Marburg)Executive
Directors Bernadette Bouchon-Meunier (LIP6, Paris, France)Ronald R. Yager (Iona College, USA)
International Advisory Board
G. Coletti, Italy C. Marsala, France L. Valverde, SpainM. Delgado, Spain M. Ojeda-Aciego, Spain J.L. Verdegay, SpainL. Foulloy, France M. Rifqi, France M.A. Vila, SpainJ. Gutierrez-Rios, Spain L. Saitta, Italy L.A. Zadeh, USAL. Magdalena, Spain E. Trillas, Spain
Special Session Organizers
P. Angelov F. Hoffmann B. Prados SuarezA. Antonucci S. Kaci M. PreußC. Beierle J. Kacprzyk A. RalescuG. Beliakov G. Kern-Isberner D. RalescuG. Bordogna C. Labreuche E. ReucherA. Bouchachia H. Legind Larsen W. RodderH. Bustince E. William De Luca S. RomanıT. Calvo E. Lughofer G. RudolphP. Carrara E. Marchioni G. RußJ. Chamorro Martınez N. Marin D. SanchezD. Coquin M. Minoh R. SeisingT. Denoeux G. Navarro-Arribas A. SkowronP. Eklund H. Son Nguyen D. SlezakZ. Elouedi V. Novak O. StraussM. Fedrizzi P. Melo Pinto E. SzmidtJ. Fernandez E. Miranda S. TerminiT. Flaminio V.A. Niskanen V. TorraL. Godo D. Ortiz-Arroyo L. ValetM. Grabisch I. Perfilieva A. VallsA.J. Grichnik O. Pons R.R. Yager
VIII Organization
International Programme Committee
Area ChairsP. Bosc, France L. Godo, Spain R. Mesiar, SloveniaO. Cordon, Spain F. Gomide, Spain D. Sanchez, SpainG. De Cooman, Belgium M. Grabisch, France R. Seising, SpainT. Denoeux, France F. Herrera, Spain R. Slowinski, PolandR. Felix, Germany L. Magdalena, Spain
Regular Members
P. Angelov, UKJ.A. Appriou, FranceM. Baczynski, PolandG. Beliakov, AustraliaS. Ben Yahia, TunisiaS. Benferat, FranceH. Berenji, USAJ. Bezdek, USAI. Bloch, FranceU. Bodenhofer, AustriaP. P. Bonissone, USAC. Borgelt, SpainH. Bustince, SpainR. Casadio, ItalyY. Chalco-Cano, ChileC.A. Coello Coello,
MexicoI. Couso, SpainB. De Baets, BelgiumG. De Tre, BelgiumM. Detyniecki, FranceD. Dubois, FranceF. Esteva, SpainM. Fedrizzi, ItalyJ. Fodor, HungaryD. Fogel, USAK. Fujimoto, JapanP. Gallinari, FranceB. Gerla, ItalyM.A. Gil, SpainS. Gottwald, GermanyS. Grossberg, USA
P. Hajek,Czech Republic
L. Hall, USAE. Herrera-Viedma,
SpainC. Noguera, SpainK. Hirota, JapanA. Hunter, UKH. Ishibuchi, JapanY. Jin, GermanyJ. Kacprzyk, PolandA. Kandel, USAG. Kern-Isberner,
GermanyE.P. Klement, AustriaL. Koczy, HungaryV. Kreinovich, USAT. Kroupa,
Czech RepublicC. Labreuche, FranceJ. Lang, FranceP. Larranaga, SpainH. Larsen, DenmarkA. Laurent, FranceM.J. Lesot, FranceC.J. Liau, TaiwanW. Lodwick, USAJ.A. Lozano, SpainT. Lukasiewicz, UKF. Marcelloni, ItalyJ.L. Marichal,
Luxembourg
N. Marin, SpainT. Martin, UKL. Martinez, SpainJ. Medina, SpainJ. Mendel, USAE. Miranda, SpainP. Miranda, SpainJ. Montero, SpainS. Moral, SpainM. Nachtegael, BelgiumY. Nojima, JapanV. Novak,
Czech RepublicH. Nurmi, FinlandE. Pap, SerbiaW. Pedrycz, CanadaF. Petry, USAV. Piuri, ItalyO. Pivert, FranceP. Poncelet, FranceH. Prade, FranceA. Ralescu, USAD. Ralescu, USAM. Ramdani, MoroccoM. Reformat, CanadaD. Ruan, BelgiumE. Ruspini, USAR. Scozzafava, ItalyP. Shenoy, USAG. Simari, ArgentinaP. Sobrevilla, SpainU. Straccia, Italy
Organization IX
T. Stutzle, BelgiumK.C. Tan, SingaporeR. Tanscheit, BrazilS. Termini, ItalyV. Torra, Spain
I.B. Turksen, CanadaB. Vantaggi, ItalyP. Vicig, ItalyZ. Wang, USAM. Zaffalon, Switzerland
H.J. Zimmermann,Germany
J. Zurada, USA
Table of Contents
Machine Learning and Data Mining
Similarity and Instinguishability
Towards a Conscious Choice of a Fuzzy Similarity Measure:A Qualitative Point of View . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Bernadette Bouchon-Meunier, Giulianella Coletti,Marie-Jeanne Lesot, and Maria Rifqi
A Stochastic Treatment of Similarity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Anca Ralescu, Sofia Visa, and Stefana Popovici
Order-Based Equivalence Degrees for Similarity and DistanceMeasures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Marie-Jeanne Lesot and Maria Rifqi
Comparing Partitions by Subset Similarities . . . . . . . . . . . . . . . . . . . . . . . . . 29Thomas A. Runkler
Finitely Valued Indistinguishability Operators . . . . . . . . . . . . . . . . . . . . . . . 39Gaspar Mayor and Jordi Recasens
Discovering Rules-Based Similarity in Microarray Data . . . . . . . . . . . . . . . 49Andrzej Janusz
Clustering and Classification
Fuzzy Clustering of Incomplete Data Based on Cluster Dispersion . . . . . . 59Ludmila Himmelspach and Stefan Conrad
Automatic Detection of Active Region on EUV Solar Images UsingFuzzy Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
M. Carmen Aranda and Carlos Caballero
On Dynamic Soft Dimension Reduction in Evolving Fuzzy Classifiers . . . 79Edwin Lughofer
Multi-class Imbalanced Data-Sets with Linguistic Fuzzy Rule BasedClassification Systems Based on Pairwise Learning . . . . . . . . . . . . . . . . . . . 89
Alberto Fernandez, Mara Jose del Jesus, and Francisco Herrera
Probabilistic Rough Set Approaches to Ordinal Classification withMonotonicity Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
Jerzy B�laszczynski, Roman S�lowinski, and Marcin Szel ↪ag
XII Table of Contents
Web Page Classification: A Probabilistic Model with RelationalUncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
Elisabetta Fersini, Enza Messina, and Francesco Archetti
Evidential Multi-Label Classification Approach to Learning from Datawith Imprecise Labels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
Zoulficar Younes, Fahed Abdallah, and Thierry Denœux
A K-Nearest Neighbours Method Based on Lower Previsions . . . . . . . . . . 129Sebastien Destercke
Statistics with Imprecise Data
Fuzzy Probabilities: Tentative Discussions on the MathematicalConcepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
Enric Trillas, Takehiko Nakama, and Itziar Garcıa-Honrado
On Dealing with Imprecise Information in a Content Based ImageRetrieval System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
Tatiana Jaworska, Janusz Kacprzyk, Nicolas Marın, andS�lawomir Zadrozny
An Extension of Stochastic Dominance to Fuzzy Random Variables . . . . 159Farid Aiche and Didier Dubois
Correlation of Intuitionistic Fuzzy Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169Eulalia Szmidt and Janusz Kacprzyk
A Correlation Ratio for Possibility Distributions . . . . . . . . . . . . . . . . . . . . . 178Robert Fuller, Jozsef Mezei, and Peter Varlaki
Data Analysis
On Nonparametric Predictive Inference for Ordinal Data . . . . . . . . . . . . . . 188Frank P.A. Coolen, Pauline Coolen-Schrijner, and Tahani A. Maturi
Using Cloudy Kernels for Imprecise Linear Filtering . . . . . . . . . . . . . . . . . . 198Sebastien Destercke and Olivier Strauss
Peakedness and Generalized Entropy for Continuous DensityFunctions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208
Ines Couso and Didier Dubois
The Most Representative Utility Function for Non-Additive RobustOrdinal Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220
Silvia Angilella, Salvatore Greco, and Benedetto Matarazzo
Table of Contents XIII
Alternative Normalization Schemas for Bayesian ConfirmationMeasures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230
Salvatore Greco, Roman S�lowinski, and Izabela Szczech
Feature Analysis
Gender and Age Estimation from Synthetic Face Images . . . . . . . . . . . . . . 240Alberto N. Escalante B. and Laurenz Wiskott
Attribute Value Selection Considering the Minimum DescriptionLength Approach and Feature Granularity . . . . . . . . . . . . . . . . . . . . . . . . . . 250
Kemal Ince and Frank Klawonn
Concept Analysis
Possibility Theory and Formal Concept Analysis: ContextDecomposition and Uncertainty Handling . . . . . . . . . . . . . . . . . . . . . . . . . . . 260
Yassine Djouadi, Didier Dubois, and Henri Prade
A Parallel between Extended Formal Concept Analysis and BipartiteGraphs Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 270
Bruno Gaume, Emmanuel Navarro, and Henri Prade
Negotiation as Creative Social Interaction Using Concept Hierarchies . . . 281Frederick E. Petry and Ronald R. Yager
Temporal Data Mining
Estimating Top-k Destinations in Data Streams . . . . . . . . . . . . . . . . . . . . . . 290Nuno Homem and Joao Paulo Carvalho
A Data Mining Algorithm for Inducing Temporal ConstraintNetworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 300
Miguel R. Alvarez, Paulo Felix, Purificacion Carinena, andAbraham Otero
Analysis of the Time Evolution of Scientograms Using the SubdueGraph Mining Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 310
Arnaud Quirin, Oscar Cordon, Prakash Shelokar, and Carmen Zarco
Short-Time Prediction Based on Recognition of Fuzzy Time SeriesPatterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 320
Gernot Herbst and Steffen F. Bocklisch
Time Series Comparison Using Linguistic Fuzzy Techniques . . . . . . . . . . . 330Rita Castillo-Ortega, Nicolas Marın, and Daniel Sanchez
XIV Table of Contents
Granular Approach for Evolving System Modeling . . . . . . . . . . . . . . . . . . . 340Daniel Leite, Pyramo Costa Jr., and Fernando Gomide
Data Mining Applications
Data Mining in Precision Agriculture: Management of SpatialInformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 350
Georg Ruß and Alexander Brenning
Fuzzy Multivariable Gaussian Evolving Approach for Fault Detectionand Diagnosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 360
Andre Lemos, Walmir Caminhas, and Fernando Gomide
Dispersion Estimates for Telecommunications Fraud . . . . . . . . . . . . . . . . . . 370Nuno Homem and Joao Paulo Carvalho
The Link Prediction Problem in Bipartite Networks . . . . . . . . . . . . . . . . . . 380Jerome Kunegis, Ernesto W. De Luca, and Sahin Albayrak
Aggregation and Fusion
Aggregation
Symmetrization of Modular Aggregation Functions . . . . . . . . . . . . . . . . . . . 390Radko Mesiar and Andrea Mesiarova-Zemankova
Smooth Aggregation Functions on Finite Scales . . . . . . . . . . . . . . . . . . . . . . 398Margalida Mas, Miquel Monserrat, and Joan Torrens
Dual Representable Aggregation Functions and Their DerivedS-Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 408
Isabel Aguilo, Marc Carbonell, Jaume Suner, and Joan Torrens
Aggregation Functions with Stronger Types of Monotonicity . . . . . . . . . . . 418Erich Peter Klement, Maddalena Manzi, and Radko Mesiar
Some Remarks on the Characterization of Idempotent Uninorms . . . . . . . 425Daniel Ruiz-Aguilera, Joan Torrens, Bernard De Baets, andJanos Fodor
On the Median and Its Extensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435Gleb Beliakov, Humberto Bustince, and Javier Fernandez
Information Fusion
Evidential Combination of Multiple HMM Classifiers for Multi-scriptHandwritting Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 445
Yousri Kessentini, Thomas Burger, and Thierry Paquet
Table of Contents XV
Using Uncertainty Information to Combine Soft Classifications . . . . . . . . . 455Luisa M.S. Goncalves, Cidalia C. Fonte, and Mario Caetano
Performance Evaluation of a Fusion System Devoted to ImageInterpretation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 464
Abdellah Lamallem, Lionel Valet, and Didier Coquin
A New Adaptive Consensus Reaching Process Based on the Experts’Importance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 474
Ignacio J. Perez, F.J. Cabrerizo, S. Alonso, and E. Herrera-Viedma
Integrals
On the Robustness for the Choquet Integral . . . . . . . . . . . . . . . . . . . . . . . . . 484Christophe Labreuche
Explicit Descriptions of Bisymmetric Sugeno Integrals . . . . . . . . . . . . . . . . 494Miguel Couceiro and Erkko Lehtonen
Learning Fuzzy-Valued Fuzzy Measures for the Fuzzy-Valued SugenoFuzzy Integral . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 502
Derek T. Anderson, James M. Keller, and Timothy C. Havens
Choquet Integration on Set Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 512U. Faigle, M. Grabisch, and M. Heyne
Necessity-Based Choquet Integrals for Sequential Decision Makingunder Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 521
Nahla Ben Amor, Helene Fargier, and Wided Guezguez
Preference Modeling
A Fuzzy-Rule-Based Approach to Contextual Preference Queries . . . . . . . 532Allel Hadjali, Amine Mokhtari, and Olivier Pivert
Extracting and Modelling Preferences from Dialogue . . . . . . . . . . . . . . . . . 542Nicholas Asher, Elise Bonzon, and Alex Lascarides
Argumentation Framework with Fuzzy Preference Relations . . . . . . . . . . . 554Souhila Kaci and Christophe Labreuche
An Algorithm for Generating Consistent and Transitive Approximationsof Reciprocal Preference Relations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 564
Steven Freson, Hans De Meyer, and Bernard De Baets
Preference Modeling and Model Management for InteractiveMulti-objective Evolutionary Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . 574
Johannes Krettek, Jan Braun, Frank Hoffmann, andTorsten Bertram
XVI Table of Contents
Dominance-Based Rough Set Approach to Preference Learning fromPairwise Comparisons in Case of Decision under Uncertainty . . . . . . . . . . 584
Salvatore Greco, Benedetto Matarazzo, and Roman S�lowinski
Uncertainty Handling
Fuzzy Methods
Trimming Plethoric Answers to Fuzzy Queries: An Approach Based onPredicate Correlation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 595
Patrick Bosc, Allel Hadjali, Olivier Pivert, and Gregory Smits
Searching Aligned Groups of Objects with Fuzzy Criteria . . . . . . . . . . . . . 605Maria Carolina Vanegas, Isabelle Bloch, and Jordi Inglada
How to Translate Words into Numbers? A Fuzzy Approach for theNumerical Translation of Verbal Probabilities . . . . . . . . . . . . . . . . . . . . . . . . 614
Franziska Bocklisch, Steffen F. Bocklisch, and Josef F. Krems
Plateau Regions: An Implementation Concept for Fuzzy Regions inSpatial Databases and GIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 624
Virupaksha Kanjilal, Hechen Liu, and Markus Schneider
Genuine Linguistic Fuzzy Logic Control: Powerful and SuccessfulControl Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 634
Vilem Novak
Cytoplasm Contour Approximation Based on Color Fuzzy Sets andColor Gradient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 645
Santiago Romanı, Belen Prados-Suarez, Pilar Sobrevilla, andEduard Montseny
Keeping Secrets in Possibilistic Knowledge Bases with Necessity-ValuedPrivacy Policies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 655
Lena Wiese
Inference with Fuzzy and Probabilistic Information . . . . . . . . . . . . . . . . . . . 665Giulianella Coletti and Barbara Vantaggi
Bayesian Networks
Modelling Patterns of Evidence in Bayesian Networks: A Case-Study inClassical Swine Fever . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 675
Linda C. van der Gaag, Janneke Bolt, Willie Loeffen, andArmin Elbers
An Importance Sampling Approach to Integrate Expert KnowledgeWhen Learning Bayesian Networks from Data . . . . . . . . . . . . . . . . . . . . . . . 685
Andres Cano, Andres R. Masegosa, and Serafın Moral
Table of Contents XVII
Belief Functions
Conflicts within and between Belief Functions . . . . . . . . . . . . . . . . . . . . . . . 696Milan Daniel
Consonant Continuous Belief Functions Conflicts Calculation . . . . . . . . . . 706Jean-Marc Vannobel
Credal Sets Approximation by Lower Probabilities: Application toCredal Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 716
Alessandro Antonucci and Fabio Cuzzolin
Rule Discovery Process Based on Rough Sets under the Belief FunctionFramework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 726
Salsabil Trabelsi, Zied Elouedi, and Pawan Lingras
Independent Natural Extension . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 737Gert de Cooman, Enrique Miranda, and Marco Zaffalon
Logics
On Elementary Extensions in Fuzzy Predicate Logics . . . . . . . . . . . . . . . . . 747Pilar Dellunde and Francesc Esteva
Logical Proportions – Typology and Roadmap . . . . . . . . . . . . . . . . . . . . . . . 757Henri Prade and Gilles Richard
Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 769
