Communicationsin Computer and Information Science 81

Eyke Hüllermeier Rudolf KruseFrank Hoffmann (Eds.)

Information Processingand Management ofUncertainty inKnowledge-Based Systems

Applications

13th International Conference, IPMU 2010Dortmund, Germany, June 28 – July 2, 2010Proceedings, Part II

13

Volume Editors

Eyke HüllermeierPhilipps-Universität MarburgMarburg, GermanyE-mail: eyke@mathematik.uni-marburg.de

Rudolf KruseOtto-von-Guericke-Universität MagdeburgMagdeburg, GermanyE-mail: kruse@iws.cs.uni-magdeburg.de

Frank HoffmannTechnische Universität DortmundDortmund, GermanyE-mail: frank.hoffmann@tu-dortmund.de

Library of Congress Control Number: 2010929196

CR Subject Classification (1998): I.2, H.3, F.1, H.4, I.5, I.4

ISSN 1865-0929ISBN-10 3-642-14057-2 Springer Berlin Heidelberg New YorkISBN-13 978-3-642-14057-0 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.

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© Springer-Verlag Berlin Heidelberg 2010Printed in Germany

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Preface

The International Conference on Information Processing and Management of Un-certainty in Knowledge-Based Systems, IPMU, is organized every two years withthe aim of bringing together scientists working on methods for the managementof uncertainty and aggregation of information in intelligent systems. Since 1986,this conference has been providing a forum for the exchange of ideas betweentheoreticians and practitioners working in these areas and related fields. The 13th

IPMU conference took place in Dortmund, Germany, June 28–July 2, 2010.This volume contains 77 papers selected through a rigorous reviewing pro-

cess. The contributions reflect the richness of research on topics within the scopeof the conference and represent several important developments, specifically fo-cused on applications of methods for information processing and managementof uncertainty in knowledge-based systems.

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 was the backbone on several organizational and elec-tronic 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.

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 Committee

General 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, Munich)

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 – Part II

Data Analysis Applications

Data-Driven Design of Takagi-Sugeno Fuzzy Systems for PredictingNOx Emissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Edwin Lughofer, Vicente Macian, Carlos Guardiola, andErich Peter Klement

Coping with Uncertainty in Temporal Gene Expressions Using SymbolicRepresentations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Silvana Badaloni and Marco Falda

Olive Trees Detection in Very High Resolution Images . . . . . . . . . . . . . . . . 21Juan Moreno-Garcia, Luis Jimenez Linares,Luis Rodriguez-Benitez, and Cayetano J. Solana-Cipres

A Fast Recursive Approach to Autonomous Detection, Identificationand Tracking of Multiple Objects in Video Streams underUncertainties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Pouria Sadeghi-Tehran, Plamen Angelov, and Ramin Ramezani

Soft Concept Hierarchies to Summarise Data Streams and HighlightAnomalous Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

Trevor Martin, Yun Shen, and Andrei Majidian

Using Enriched Ontology Structure for Improving Statistical Models ofGene Annotation Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

Frank Rugheimer

Predicting Outcomes of Septic Shock Patients Using Feature SelectionBased on Soft Computing Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

Andre S. Fialho, Federico Cismondi, Susana M. Vieira,Joao M.C. Sousa, Shane R. Reti, Michael D. Howell, andStan N. Finkelstein

Obtaining the Compatibility between Musicians Using SoftComputing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

Teresa Leon and Vicente Liern

Consistently Handling Geographical User Data: Context-DependentDetection of Co-located POIs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

Guy De Tre, Antoon Bronselaer, Tom Matthe,Nico Van de Weghe, and Philippe De Maeyer

XII Table of Contents – Part II

Intelligent Databases

A Model Based on Outranking for Database Preference Queries . . . . . . . . 95Patrick Bosc, Olivier Pivert, and Gregory Smits

Incremental Membership Function Updates . . . . . . . . . . . . . . . . . . . . . . . . . 105Narjes Hachani, Imen Derbel, and Habib Ounelli

A New Approach for Comparing Fuzzy Objects . . . . . . . . . . . . . . . . . . . . . . 115Yasmina Bashon, Daniel Neagu, and Mick J. Ridley

Generalized Fuzzy Comparators for Complex Data in a FuzzyObject-Relational Database Management System . . . . . . . . . . . . . . . . . . . . 126

Juan Miguel Medina, Carlos D. Barranco, Jesus R. Campana, andSergio Jaime-Castillo

The Bipolar Semantics of Querying Null Values in Regular and FuzzyDatabases: Dealing with Inapplicability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

Tom Matthe and Guy De Tre

Describing Fuzzy DB Schemas as Ontologies: A System ArchitectureView . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

Carmen Martınez-Cruz, Ignacio J. Blanco, and M. Amparo Vila

Using Textual Dimensions in Data Warehousing Processes . . . . . . . . . . . . 158M.J. Martın-Bautista, C. Molina, E. Tejeda, and M. Amparo Vila

Information Fusion

Uncertainty Estimation in the Fusion of Text-Based Information forSituation Awareness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168

Kellyn Rein, Ulrich Schade, and Silverius Kawaletz

Aggregation of Partly Inconsistent Preference Information . . . . . . . . . . . . . 178Rudolf Felix

Risk Neutral Valuations Based on Partial Probabilistic Information . . . . 188Andrea Capotorti, Giuliana Regoli, and Francesca Vattari

A New Contextual Discounting Rule for Lower Probabilities . . . . . . . . . . . 198Sebastien Destercke

The Power Average Operator for Information Fusion . . . . . . . . . . . . . . . . . 208Ronald R. Yager

Performance Comparison of Fusion Operators in Bimodal RemoteSensing Snow Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221

Aureli Soria-Frisch, Antonio Repucci, Laura Moreno, andMarco Caparrini

Table of Contents – Part II XIII

Color Recognition Enhancement by Fuzzy Merging . . . . . . . . . . . . . . . . . . . 231Vincent Bombardier, Emmanuel Schmitt, and Patrick Charpentier

Towards a New Generation of Indicators for Consensus ReachingSupport Using Type-2 Fuzzy Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241

Witold Pedrycz, Janusz Kacprzyk, and S�lawomir Zadrozny

Decision Support

Modelling Collective Choices

Multiagent Decision Making, Fuzzy Prevision, and Consensus . . . . . . . . . . 251Antonio Maturo and Aldo G.S. Ventre

A Categorical Approach to the Extension of Social Choice Functions . . . 261Patrik Eklund, Mario Fedrizzi, and Hannu Nurmi

Signatures for Assessment, Diagnosis and Decision-Making in Ageing . . . 271Patrik Eklund

Fuzzy Decision Theory

A Default Risk Model in a Fuzzy Framework . . . . . . . . . . . . . . . . . . . . . . . . 280Hiroshi Inoue and Masatoshi Miyake

On a Fuzzy Weights Representation for Inner Dependence AHP . . . . . . . . 289Shin-ichi Ohnishi, Takahiro Yamanoi, and Hideyuki Imai

Different Models with Fuzzy Random Variables in Single-Stage DecisionProblems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 298

Luis J. Rodrıguez-Muniz and Miguel Lopez-Dıaz

Applications in Finance

A Neuro-Fuzzy Decision Support System for Selection of Small ScaleBusiness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306

Rajendra Akerkar and Priti Srinivas Sajja

Bond Management: An Application to the European Market . . . . . . . . . . 316Jose Manuel Brotons

Estimating the Brazilian Central Bank’s Reaction Function by FuzzyInference System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324

Ivette Luna, Leandro Maciel, Rodrigo Lanna F. da Silveira, andRosangela Ballini

XIV Table of Contents – Part II

Fuzzy Systems

Philosophical Aspects

Do Uncertainty and Fuzziness Present Themselves (and Behave) in theSame Way in Hard and Human Sciences? . . . . . . . . . . . . . . . . . . . . . . . . . . . 334

Settimo Termini

Some Notes on the Value of Vagueness in Everyday Communication . . . . 344Nora Kluck

On Zadeh’s “The Birth and Evolution of Fuzzy Logic” . . . . . . . . . . . . . . . . 350Yucel Yuksel

Complexity and Fuzziness in 20th Century Science and Technology . . . . . 356Rudolf Seising

Educational Software of Fuzzy Logic and Control . . . . . . . . . . . . . . . . . . . . 366Jose Galindo and Enrique Leon-Gonzalez

Fuzzy Numbers

A Fuzzy Distance between Two Fuzzy Numbers . . . . . . . . . . . . . . . . . . . . . . 376Saeid Abbasbandy and Saeide Hajighasemi

On the Jaccard Index with Degree of Optimism in Ranking FuzzyNumbers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383

Nazirah Ramli and Daud Mohamad

Negation Functions in the Set of Discrete Fuzzy Numbers . . . . . . . . . . . . . 392Jaume Casasnovas and J. Vicente Riera

Trapezoidal Approximation of Fuzzy Numbers Based on SampleData . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402

Przemys�law Grzegorzewski

Multiple Products and Implications in Interval-Valued Fuzzy SetTheory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 412

Glad Deschrijver

Fuzzy Ontology and Information Granulation: An Approach toKnowledge Mobilisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 420

Christer Carlsson, Matteo Brunelli, and Jozsef Mezei

Adjoint Pairs on Interval-Valued Fuzzy Sets . . . . . . . . . . . . . . . . . . . . . . . . . 430Jesus Medina

Table of Contents – Part II XV

Fuzzy Arithmetic

Optimistic Arithmetic Operators for Fuzzy and Gradual Intervals -Part I: Interval Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 440

Reda Boukezzoula and Sylvie Galichet

Optimistic Arithmetic Operators for Fuzzy and Gradual Intervals -Part II: Fuzzy and Gradual Interval Approach . . . . . . . . . . . . . . . . . . . . . . . 451

Reda Boukezzoula and Sylvie Galichet

Model Assessment Using Inverse Fuzzy Arithmetic . . . . . . . . . . . . . . . . . . . 461Thomas Haag and Michael Hanss

New Tools in Fuzzy Arithmetic with Fuzzy Numbers . . . . . . . . . . . . . . . . . 471Luciano Stefanini

Fuzzy Equations

Application of Gaussian Quadratures in Solving Fuzzy FredholmIntegral Equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 481

M. Khezerloo, Tofigh Allahviranloo, Soheil Salahshour,M. Khorasani Kiasari, and S. Haji Ghasemi

Existence and Uniqueness of Solutions of Fuzzy VolterraIntegro-differential Equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 491

Saeide Hajighasemi, Tofigh Allahviranloo, M. Khezerloo,M. Khorasany, and Soheil Salahshour

Expansion Method for Solving Fuzzy Fredholm-Volterra IntegralEquations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 501

S. Khezerloo, Tofigh Allahviranloo, S. Haji Ghasemi,Soheil Salahshour, M. Khezerloo, and M. Khorasan Kiasary

Solving Fuzzy Heat Equation by Fuzzy Laplace Transforms . . . . . . . . . . . . 512Soheil Salahshour and Elnaz Haghi

A New Approach for Solving First Order Fuzzy Differential Equation . . . 522Tofigh Allahviranloo and Soheil Salahshour

Soft Computing Applications

Image Processing

A Comparison Study of Different Color Spaces in Clustering BasedImage Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 532

Aranzazu Jurio, Miguel Pagola, Mikel Galar,Carlos Lopez-Molina, and Daniel Paternain

XVI Table of Contents – Part II

Retrieving Texture Images Using Coarseness Fuzzy Partitions . . . . . . . . . 542Jesus Chamorro-Martınez, Pedro Manuel Martınez-Jimenez, andJose Manuel Soto-Hidalgo

A Fuzzy Regional-Based Approach for Detecting Cerebrospinal FluidRegions in Presence of Multiple Sclerosis Lesions . . . . . . . . . . . . . . . . . . . . . 552

Francesc Xavier Aymerich, Eduard Montseny, Pilar Sobrevilla, andAlex Rovira

Probabilistic Scene Models for Image Interpretation . . . . . . . . . . . . . . . . . . 562Alexander Bauer

Motion Segmentation Algorithm for Dynamic Scenes over H.264Video . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 572

Cayetano J. Solana-Cipres, Luis Rodriguez-Benitez,Juan Moreno-Garcia, and L. Jimenez-Linares

Using Stereo Vision and Fuzzy Systems for Detecting and TrackingPeople . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 582

Rui Paul, Eugenio Aguirre, Miguel Garcıa-Silvente, andRafael Munoz-Salinas

Privacy and Security

Group Anonymity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 592Oleg Chertov and Dan Tavrov

Anonymizing Categorical Data with a Recoding Method Based onSemantic Similarity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 602

Sergio Martınez, Aida Valls, and David Sanchez

Addressing Complexity in a Privacy Expert System . . . . . . . . . . . . . . . . . . 612Siani Pearson

Privacy-Protected Camera for the Sensing Web . . . . . . . . . . . . . . . . . . . . . . 622Ikuhisa Mitsugami, Masayuki Mukunoki, Yasutomo Kawanishi,Hironori Hattori, and Michihiko Minoh

Bayesian Network-Based Approaches for Severe Attack Prediction andHandling IDSs’ Reliability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 632

Karim Tabia and Philippe Leray

The Sensing Web

Structuring and Presenting the Distributed Sensory Information in theSensing Web . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 643

Rin-ichiro Taniguchi, Atsushi Shimada, Yuji Kawaguchi,Yousuke Miyata, and Satoshi Yoshinaga

Table of Contents – Part II XVII

Evaluation of Privacy Protection Techniques for Speech Signals . . . . . . . . 653Kazumasa Yamamoto and Seiichi Nakagawa

Digital Diorama: Sensing-Based Real-World Visualization . . . . . . . . . . . . . 663Takumi Takehara, Yuta Nakashima, Naoko Nitta, andNoboru Babaguchi

Personalizing Public and Privacy-Free Sensing Information with aPersonal Digital Assistant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 673

Takuya Kitade, Yasushi Hirano, Shoji Kajita, and Kenji Mase

The Open Data Format and Query System of the Sensing Web . . . . . . . . 680Naruki Mitsuda and Tsuneo Ajisaka

See-Through Vision: A Visual Augmentation Method forSensing-Web . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 690

Yuichi Ohta, Yoshinari Kameda, Itaru Kitahara,Masayuki Hayashi, and Shinya Yamazaki

Manufacturing and Scheduling

Manufacturing Virtual Sensors at Caterpillar, Inc. . . . . . . . . . . . . . . . . . . . . 700Timothy J. Felty, James R. Mason, and Anthony J. Grichnik

Modelling Low-Carbon UK Energy System Design through 2050 in aCollaboration of Industry and the Public Sector . . . . . . . . . . . . . . . . . . . . . 709

Christopher Heaton and Rod Davies

A Remark on Adaptive Scheduling of Optimization Algorithms . . . . . . . . 719Krisztian Balazs and Laszlo T. Koczy

An Adaptive Fuzzy Model Predictive Control System for the TextileFiber Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 729

Stefan Berlik and Maryam Nasiri

Methodology for Evaluation of Linked Multidimensional MeasurementSystem with Balanced Scorecard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 737

Yutaka Kigawa, Kiyoshi Nagata, Fuyume Sai, and Michio Amagasa

Predictive Probabilistic and Possibilistic Models Used for RiskAssessment of SLAs in Grid Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 747

Christer Carlsson and Robert Fuller

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 759

Table of Contents – Part I

Reasoning with Uncertainty

Decomposable Models

An Algorithm to Find a Perfect Map for Graphoid Structures . . . . . . . . . 1Marco Baioletti, Giuseppe Busanello, and Barbara Vantaggi

An Empirical Study of the Use of the Noisy-Or Model in a Real-LifeBayesian Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Janneke H. Bolt and Linda C. van der Gaag

Possibilistic Graphical Models and Compositional Models . . . . . . . . . . . . . 21Jirina Vejnarova

Bayesian Networks vs. Evidential Networks: An Application to ConvoyDetection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

Evangeline Pollard, Michele Rombaut, and Benjamin Pannetier

Approximation of Data by Decomposable Belief Models . . . . . . . . . . . . . . . 40Radim Jirousek

Imprecise Probabilities

A Gambler’s Gain Prospects with Coherent Imprecise Previsions . . . . . . . 50Paolo Vicig

Infinite Exchangeability for Sets of Desirable Gambles . . . . . . . . . . . . . . . . 60Gert de Cooman and Erik Quaeghebeur

Ergodicity Conditions for Upper Transition Operators . . . . . . . . . . . . . . . . 70Filip Hermans and Gert de Cooman

An Empirical Comparison of Bayesian and Credal Set Theory forDiscrete State Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

Alexander Karlsson, Ronnie Johansson, and Sten F. Andler

On the Complexity of Non-reversible Betting Games on Many-ValuedEvents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

Martina Fedel and Tommaso Flaminio

XX Table of Contents – Part I

Sequential Decision Processes under Act-State Independence withArbitrary Choice Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

Matthias C.M. Troffaes, Nathan Huntley, and Ricardo Shirota Filho

Logics for Reasoning

Similarity-Based Equality with Lazy Evaluation . . . . . . . . . . . . . . . . . . . . . . 108Gines Moreno

Progressive Reasoning for Complex Dialogues among Agents . . . . . . . . . . 118Josep Puyol-Gruart and Mariela Morveli-Espinoza

Measuring Instability in Normal Residuated Logic Programs: DiscardingInformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

Nicolas Madrid and Manuel Ojeda-Aciego

Implementing Prioritized Merging with ASP . . . . . . . . . . . . . . . . . . . . . . . . . 138Julien Hue, Odile Papini, and Eric Wurbel

Preference Modeling

An Interactive Algorithm to Deal with Inconsistencies in theRepresentation of Cardinal Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148

Brice Mayag, Michel Grabisch, and Christophe Labreuche

Characterization of Complete Fuzzy Preorders Defined by Archimedeant-Norms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158

Ignacio Montes, Davide Martinetti, Susana Dıaz, and Susana Montes

Rectification of Preferences in a Fuzzy Environment . . . . . . . . . . . . . . . . . . 168Camilo Franco de los Rıos, Javier Montero, andJ. Tinguaro Rodrıguez

Data Analysis and Knowledge Processing

Belief Functions

Identification of Speakers by Name Using Belief Functions . . . . . . . . . . . . . 179Simon Petitrenaud, Vincent Jousse, Sylvain Meignier, andYannick Esteve

Constructing Multiple Frames of Discernment for MultipleSubproblems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189

Johan Schubert

Conflict Interpretation in a Belief Interval Based Framework . . . . . . . . . . . 199Clement Solau, Anne-Marie Jolly, Laurent Delahoche,Bruno Marhic, and David Menga

Table of Contents – Part I XXI

Evidential Data Association Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209Ahmed Dallil, Mourad Oussalah, and Abdelaziz Ouldali

Maintaining Evidential Frequent Itemsets in Case of Data Deletion . . . . . 218Mohamed Anis Bach Tobji and Boutheina Ben Yaghlane

TS-Models from Evidential Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228Rui Jorge Almeida and Uzay Kaymak

Measuring Impact of Diversity of Classifiers on the Accuracy ofEvidential Ensemble Classifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238

Yaxin Bi and Shengli Wu

Multiplication of Multinomial Subjective Opinions . . . . . . . . . . . . . . . . . . . 248Audun Jøsang and Stephen O’Hara

Evaluation of Information Reported: A Model in the Theory ofEvidence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 258

Laurence Cholvy

Rough Sets

Gradual Evaluation of Granules of a Fuzzy Relation: R-related Sets . . . . 268Slavka Bodjanova and Martin Kalina

Combined Bayesian Networks and Rough-Granular Approaches forDiscovery of Process Models Based on Vehicular Traffic Simulation . . . . . 278

Mateusz Adamczyk, Pawe�l Betlinski, and Pawe�l Gora

On Scalability of Rough Set Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 288Piotr Kwiatkowski, Sinh Hoa Nguyen, and Hung Son Nguyen

Machine Learning

Interestingness Measures for Association Rules within Groups . . . . . . . . . 298Aıda Jimenez, Fernando Berzal, and Juan-Carlos Cubero

Data Mining in RL-Bags . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 308M. Dolores Ruiz, Miguel Delgado, and Daniel Sanchez

Feature Subset Selection for Fuzzy Classification Methods . . . . . . . . . . . . . 318Marcos E. Cintra and Heloisa A. Camargo

Restricting the IDM for Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 328Giorgio Corani and Alessio Benavoli

XXII Table of Contents – Part I

Probabilistic Methods

Estimation of Possibility-Probability Distributions . . . . . . . . . . . . . . . . . . . 338Balapuwaduge Sumudu Udaya Mendis and Tom D. Gedeon

Bayesian Assaying of GUHA Nuggets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 348Robert Piche and Esko Turunen

Rank Correlation Coefficient Correction by Removing Worst Cases . . . . . 356Martin Krone and Frank Klawonn

Probabilistic Relational Learning for Medical Diagnosis Based on IonMobility Spectrometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365

Marc Finthammer, Christoph Beierle, Jens Fisseler,Gabriele Kern-Isberner, Bulent Moller, and Jorg I. Baumbach

Automated Gaussian Smoothing and Peak Detection Based onRepeated Averaging and Properties of a Spectrum’s Curvature . . . . . . . . 376

Hyung-Won Koh and Lars Hildebrand

Uncertainty Interval Expression of Measurement: Possibility MaximumSpecificity versus Probability Maximum Entropy Principles . . . . . . . . . . . . 386

Gilles Mauris

Fuzzy Methods

Lazy Induction of Descriptions Using Two Fuzzy Versions of the RandIndex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 396

Eva Armengol and Angel Garcıa-Cerdana

Fuzzy Clustering-Based Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 406Luiz F.S. Coletta, Eduardo R. Hruschka, Thiago F. Covoes, andRicardo J.G.B. Campello

Fuzzy Classification of Nonconvex Data-Inherent Structures . . . . . . . . . . . 416Arne-Jens Hempel and Steffen F. Bocklisch

Fuzzy-Pattern-Classifier Training with Small Data Sets . . . . . . . . . . . . . . . 426Uwe Monks, Denis Petker, and Volker Lohweg

Temporal Linguistic Summaries of Time Series Using Fuzzy Logic . . . . . . 436Janusz Kacprzyk and Anna Wilbik

A Comparison of Five Fuzzy Rand Indices . . . . . . . . . . . . . . . . . . . . . . . . . . 446Derek T. Anderson, James C. Bezdek, James M. Keller, andMihail Popescu

Identifying the Risk of Attribute Disclosure by Mining Fuzzy Rules . . . . . 455Irene Dıaz, Jose Ranilla, Luis J. Rodrıguez-Muniz, and Luigi Troiano

Table of Contents – Part I XXIII

Fuzzy Sets and Fuzzy Logic

Fuzzy Measures and Integrals

Explicit Descriptions of Associative Sugeno Integrals . . . . . . . . . . . . . . . . . 465Miguel Couceiro and Jean-Luc Marichal

Continuity of Choquet Integrals of Supermodular Capacities . . . . . . . . . . . 471Nobusumi Sagara

Inclusion-Exclusion Integral and Its Application to Subjective VideoQuality Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 480

Aoi Honda and Jun Okamoto

Fuzzy Measure Spaces Generated by Fuzzy Sets . . . . . . . . . . . . . . . . . . . . . . 490Antonın Dvorak and Michal Holcapek

Absolute Continuity of Monotone Measure and Convergence inMeasure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 500

Jun Li, Radko Mesiar, and Qiang Zhang

An Axiomatic Approach to Fuzzy Measures Like Set Cardinality forFinite Fuzzy Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 505

Michal Holcapek

Choquet-integral-Based Evaluations by Fuzzy Rules: Methods forDeveloping Fuzzy Rule Tables on the Basis of Weights and InteractionDegrees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 515

Eiichiro Takahagi

Fuzzy Inference

On a New Class of Implications in Fuzzy Logic . . . . . . . . . . . . . . . . . . . . . . 525Yun Shi, Bart Van Gasse, Da Ruan, and Etienne Kerre

Diagrams of Fuzzy Orderings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 535Branimir Seselja and Andreja Tepavcevic

Fuzzy Relation Equations in Semilinear Spaces . . . . . . . . . . . . . . . . . . . . . . 545Irina Perfilieva

Adaptive Rule Based-Reasoning by Qualitative Analysis . . . . . . . . . . . . . . 553Marius Mircea Balas and Valentina Emilia Balas

Fuzzy Regions: Adding Subregions and the Impact on Surface andDistance Calculation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 561

Jorg Verstraete

XXIV Table of Contents – Part I

On Liu’s Inference Rules for Fuzzy Inference Systems . . . . . . . . . . . . . . . . . 571Xin Gao, Dan A. Ralescu, and Yuan Gao

Intuitionistic Fuzzy Sets

A New Approach to the Distances between Intuitionistic Fuzzy Sets . . . . 581Krassimir Atanassov

Atanassov’s Intuitionistic Contractive Fuzzy Negations . . . . . . . . . . . . . . . . 591Benjamin Bedregal, Humberto Bustince, Javier Fernandez,Glad Deschrijver, and Radko Mesiar

Trust Propagation Based on Group Opinion . . . . . . . . . . . . . . . . . . . . . . . . . 601Anna Stachowiak

Application of IF-Sets to Modeling of Lip Shapes Similarities . . . . . . . . . . 611Krzysztof Dyczkowski

A Random Set and Prototype Theory Interpretation of IntuitionisticFuzzy Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 618

Jonathan Lawry

Hesitation Degrees as the Size of Ignorance Combined with Fuzziness . . . 629Maciej Wygralak

On the Distributivity of Implication Operations over t-Representablet-Norms Generated from Strict t-Norms in Interval-Valued Fuzzy SetsTheory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 637

Micha�l Baczynski

Properties of Interval-Valued Fuzzy Relations, Atanassov’s Operatorsand Decomposable Operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 647

Barbara Pekala

Cardinality and Entropy for Bifuzzy Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . 656Vasile Patrascu

Aggregation Functions

Some Remarks on the Solutions to the Functional EquationI(x, y) = I(x, I(x, y)) for D-Operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 666

Sebastia Massanet and Joan Torrens

On an Open Problem of U. Hohle - A Characterization of ConditionallyCancellative T-Subnorms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 676

Balasubramaniam Jayaram

Table of Contents – Part I XXV

Triangular Norms and Conorms on the Set of Discrete FuzzyNumbers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 683

Jaume Casasnovas and J. Vicente Riera

Arity-Monotonic Extended Aggregation Operators . . . . . . . . . . . . . . . . . . . 693Marek G ↪agolewski and Przemys�law Grzegorzewski

Some Properties of Multi–argument Distances and FermatMultidistance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 703

Javier Martın and Gaspar Mayor

Mixture Utility in General Insurance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 712Jana Spirkova

Evolutionary Agorithms

Application of Evolutionary Algorithms to the Optimization of theFlame Position in Coal-Fired Utility Steam Generators . . . . . . . . . . . . . . . 722

W. Kastner, R. Hampel, T. Forster, M. Freund, M. Wagenknecht,D. Haake, H. Kanisch, U.-S. Altmann, and F. Muller

Measurement of Ground-Neutral Currents in Three Phase TransformersUsing a Genetically Evolved Shaping Filter . . . . . . . . . . . . . . . . . . . . . . . . . . 731

Luciano Sanchez and Ines Couso

A Genetic Algorithm for Feature Selection and Granularity Learningin Fuzzy Rule-Based Classification Systems for Highly ImbalancedData-Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 741

Pedro Villar, Alberto Fernandez, and Francisco Herrera

Learning of Fuzzy Rule-Based Meta-schedulers for Grid Computingwith Differential Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 751

R.P. Prado, S. Garcıa-Galan, J.E. Munoz Exposito,A.J. Yuste, and S. Bruque

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 761