Communicationsin Computer and Information Science 297

Salvatore Greco Bernadette Bouchon-MeunierGiulianella Coletti Mario FedrizziBenedetto Matarazzo Ronald R. Yager (Eds.)

Advancesin ComputationalIntelligence

14th International Conferenceon Information Processing and Managementof Uncertainty in Knowledge-Based SystemsIPMU 2012Catania, Italy, July 9-13, 2012Proceedings, Part I

13

Volume Editors

Salvatore GrecoUniversity of Catania, ItalyE-mail: salgreco@unict.it

Bernadette Bouchon-MeunierUniversity Pierre et Marie Curie, Paris, FranceE-mail: bernadette.bouchon-meunier@lip6.fr

Giulianella ColettiUniversity of Perugia, ItalyE-mail: coletti@dmi.unipg.it

Mario FedrizziUniversity of Trento, ItalyE-mail: mario.fedrizzi@unitn.it

Benedetto MatarazzoUniversity of Catania, ItalyE-mail: matarazz@unict.it

Ronald R. YagerIONA College, New Rochelle, NY, USAE-mail: ryager@iona.edu

ISSN 1865-0929 e-ISSN 1865-0937ISBN 978-3-642-31708-8 e-ISBN 978-3-642-31709-5DOI 10.1007/978-3-642-31709-5Springer Heidelberg Dordrecht London New York

Library of Congress Control Number: Applied for

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

© Springer-Verlag Berlin Heidelberg 2012This 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.The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply,even in the absence of a specific statement, that such names are exempt from the relevant protective lawsand regulations and therefore free for general use.

Typesetting: Camera-ready by author, data conversion by Scientific Publishing Services, Chennai, India

Printed on acid-free paper

Springer is part of Springer Science+Business Media (www.springer.com)

Preface

We are glad to present the proceedings of the IPMU 2012 conference (Interna-tional Conference on Information Processing and Management of Uncertainty inKnowledge-Based Systems) held in Catania, Italy, during July 9–13, 2012. TheIPMU conference is organized every two years with the focus of bringing togetherscientists working on methods for the management of uncertainty and aggrega-tion of information in intelligent systems. This conference provides a medium forthe exchange of ideas between theoreticians and practitioners in these and relatedareas. This was the 14th edition of the IPMU conference, which started in 1986and has been held every two years in the following locations in Europe: Paris(1986), Urbino (1988), Paris (1990), Palma de Mallorca (1992), Paris (1994),Granada (1996), Paris (1998), Madrid (2000), Annecy (2002), Perugia (2004),Paris (2006), Malaga (2008), Dortmund (2010). Among the plenary speakers ofpast IPMU conferences there are three Nobel Prize winners: Kenneth Arrow,Daniel Kahneman, Ilya Prigogine.

The program of IPMU 2012 consisted of six invited talks together with 258contributed papers, authored by researchers from 36 countries, including theregular track and 35 special sessions. The invited talks were given by the fol-lowing distinguished researchers: Kalyanmoy Deb (Indian Institute of Technol-ogy Kanpur, India), Antonio Di Nola (University of Salerno, Italy), ChristopheMarsala (Universite Pierre et Marie Curie, France), Roman Slowinski (PoznanUniversity of Technology, Poland), Tomohiro Takagi (Meiji University, Japan),Peter Wakker (Erasmus University, The Netherlands). Michio Sugeno receivedthe Kampe de Feriet Award, granted every two years on the occasion of theIPMU conference, in view of his eminent research contributions to the handlingof uncertainty through fuzzy measures and fuzzy integrals, and fuzzy controlusing fuzzy systems.

The success of such an event is mainly due to the hard work and dedica-tion of a number of people and the collaboration of several institutions. Wewant to acknowledge the help of the members of the International ProgramCommittee, the additional reviewers, the organizers of special sessions, and thevolunteer students. All of them deserve many thanks for having helped to attainthe goal of providing a balanced event with a high level of scientific exchangeand a pleasant environment. A special mention is deserved by Silvia Angilella,Salvatore Corrente, Fabio Rindone, and Giuseppe Vaccarella, who contributedgreatly to the organization of the conference and especially to the review process.

VI Preface

We acknowledge the use of the EasyChair conference system for the paper sub-mission and review. We would also like to thank Alfred Hofmann and LeonieKunz, and Springer, for providing continuous assistance and ready advice when-ever needed.

May 2012 Salvatore GrecoBernadette Bouchon-Meunier

Giulianella ColettiMario Fedrizzi

Benedetto MatarazzoRonald R. Yager

Organization

Conference Committee

General Chair

Salvatore Greco University of Catania, Italy

Co-chairsGiulianella Coletti University of Perugia, ItalyMario Fedrizzi University of Trento, ItalyBenedetto Matarazzo University of Catania, Italy

Executive DirectorsBernadette Bouchon-Meunier LIP6, Paris, FranceRonald R. Yager Iona College, USA

Special Session Organizers

Alessandro AntonucciMichal BaczynskiEdurne BarrenecheaSebastiano BattiatoJan BazanAbdelhamid BouchachiaHumberto BustineDavid CarfıDavide CiucciJesus ChamorroGiulianella ColettiDidier CoquinAlfredo CuzzocreaGiovanni Battista

DagninoDidier DuboisFabrizio DuranteZied EloudiMacarena EspinillaGisella FacchinettiJavier FernandezTommaso FlaminioGiovanni Gallo

Roberto GhiselliRicci

Karina GibertGiovanni GiuffridaMichel GrabischPrzemyslaw

GrzegorzewskiMaria Letizia GuerraFrancisco HerreraBalasubramaniam

JayaramJanusz KacprzykCengiz KahramanCristophe LabreucheIoana LeusteanEdwin LughoferEnrico MarchioniNicolas MarinLuis MartinezPedro Melo-PintoRadko MesiarEnrique MirandaAntonio Moreno

Moamar SayedMouchaweh

GuillermoNavarro-Arribas

Vesa NiskanenMiguel PagolaOlga PonsAna PraderaAnca RalescuDaniel SanchezMiquel Sanchez-MarreRudolf SeisingAndrzej SkowronDominik SlezakHung Son NguyenCarlo SempiLuciano StefaniniEulalia SzmidtMarco Elio TabacchiVicenc TorraGracian TrivinoLionel ValetAida Valls

VIII Organization

International Program Committee

J. Aczel (Canada)J. Bezdek (USA)P. Bonissone (USA)G. Chen (China)V. Cross (USA)B. De Baets (Belgium)T. Denoeux (France)M. Detyniecki (France)A. Di Nola (Italy)D. Dubois (France)F. Esteva (Spain)J. Fodor (Hungary)S. Galichet (France)P. Gallinari (France)M.A. Gil (Spain)F. Gomide (Brazil)M. Grabisch (France)S. Grossberg (USA)P. Hajek

(Czech Republic)

L. Hall (USA)F. Herrera (Spain)K. Hirota (Japan)F. Hoffmann (Germany)J. Kacprzyk (Poland)A. Kandel (USA)J. Keller (USA)F. Klawonn (Germany)E.P. Klement (Austria)L. Koczy (Hungary)V. Kreinovich (USA)R. Kruse (Germany)H. Larsen (Denmark)M.-J. Lesot (France)T. Martin (UK)J. Mendel (USA)R. Mesiar (Slovakia)S. Moral (Spain)H.T. Nguyen (USA)S. Ovchinnikov (USA)

G. Pasi (Italy)W. Pedrycz (Canada)V. Piuri (Italy)O. Pivert (France)H. Prade (France)A. Ralescu (USA)D. Ralescu (USA)M. Ramdani (Maroc)E. Ruspini (Spain)S. Sandri (Brasil)M. Sato (Japan)G. Shafer (USA)P. Shenoy (USA)P. Sobrevilla (Spain)M. Sugeno (Japan)E. Szmidt (Poland)S. Termini (Italy)I.B. Turksen (Canada)S. Zadrozny (Poland)

We thank the precious support of all the referees, which helped to improve thescientific quality of the papers submitted to the conference:

Daniel AbrilTofigh AllahviranlooCecilio AnguloAlessandro AntonucciLuca AnzilliRaouia AyachiMichal BaczynskiValentina Emilia BalasRosangela BalliniAdrian BanMohua BanerjeeCarlos D. BarrancoSebastiano BattiatoJan BazanBenjamin BedregalGleb BeliakovNahla Ben AmorSarah Ben AmorAlessio Benavoli

Ilke BereketliVeronica BiazzoIsabelle BlochFernando BobilloAndrea BoccutoGloria BordognaSilvia BortotImen BoukhrisJuergen BrankeWerner BrockmannAntoon BronselaerMatteo BrunelliAlberto BugarınHumberto BustinceTomasa CalvoDomenico CandeloroAndrea CapotortiMarta CardinFabrizio Caruso

Bice CavalloNihan Cetin DemirelEmre CevikcanMihir ChakrabortyDavide CiucciLavinia Corina CiunguVincent ClivilleGiulianella ColettiDante ContiDidier CoquinGiorgio CoraniChris CornelisMiguel CouceiroPedro CoutoAlfredo CuzzocreaNuzillard DanielleBernard De BaetsGert De CoomanYves De Smet

Organization IX

Guy De TreRoberto De VirgilioTufan DemirelGlad DeschrijverSebastien DesterckeLuigi Di GaetanoIrene DiazJozsef DombiMichael DoumposAntonio DouradoJozef DrewniakDidier DuboisFabrizio DuranteAntonin DvorakKrzysztof DyczkowskiSusana DıazZied ElouediMujde Erol GenevoisMacarena EspinillaGisella FacchinettiSalvatore FedericoMichele FedrizziJavier FernandezJuan Fernandez-SanchezValentina FerrettiJose Rui FigueiraTommaso FlaminioVito FragnelliCamilo FrancoRobert FullerMarek GagolewskiGiovanni GalloLuis GarmendiaGeorge GeorgescuBrunella GerlaKarina GibertAngelo GilioSilvio GioveLluis GodoFernando GomideMichel GrabischPrzemyslaw

GrzegorzewskiJerzy Grzymala-BusseMaria Letizia Guerra

Manuel Gomez-OlmedoRobert HableAllel HadjaliXingxing HeGernot HerbstFrancisco HerreraShoji HiranoMichal HolcapekEyke HuellermeierDusan HusekJulia InthornMasahiro InuiguchiDavid IsernAlessio IshizakaVladimir JanisJouni JarvinenPiotr JaworskiBalasubramaniam

JayaramRadim JirousekOzgur KabakJanusz KacprzykCengiz KahramanMartin KalinaErich Peter KlementAnna KolesarovaBeata KonikowskaTomas KroupaPavol Kral’Pierre KunschChristophe LabreucheFabio LamantiaFabrizio LanzafameEric LefevreKarim LidouhPawan LingrasWeiru LiuCarlos Lopez-MolinaMaite Lopez-SanchezLorenzo Di SilvestroEdwin LughoferLina MallozziMaddalena ManziEnrico MarchioniJean-Luc Marichal

Ricardo AlbertoMarques Pereira

Christophe MarsalaArnaud MartinLuis MartinezMurakami MasayukiAndres R. MasegosaSebastia MassanetTom MattheJorma K. MattilaDenis Maua’Gilles MaurisBrice MayagGaspar MayorAngelo MazzaJuan Miguel MedinaDavid MercierRadko MesiarEnrico MessinaEnrique MirandaPedro MirandaJavier MonteroIgnacio MontesSusana MontesJacky MontmainSerafin MoralAntonio MorenoMasayuki MukunokiFrancesco MusolinoKazuaki NakamuraJuan Carlos NievesSatoshi NishiguchiVesa NiskanenCarles NogueraVilem NovakPiotr NowakHannu NurmiAnnamaria OlivieriWassila OuerdaneKrzysztof PancerzEndre PapPere PardoAna PassuelloDaniel PaternainSimon Petitrenaud

X Organization

David Picado MuinoOlivier PivertOlga PonsHenri PradeAna PraderaMahardhika PratamaGiovanni PuglisiAntonio PunzoBarbara P ↪ekalaAnca RalescuFahimeh RamezaniDaniele RavıMohammad RawashdehRenata ReiserMagdalena RencovaSilja RenooijHana RezankovaAngela RicciardelloMaria RifqiJ. Tinguaro RodrıguezRosa M. RodrıguezAntoine RollandNils RosemannRafael RumiNobusumi SagaraAntonio Salmeron

Giuseppe SanfilippoJose SantamariaJose Antonio Sanz

DelgadoMoamar

Sayed-MouchawehFlorence SedesRudolf SeisingCarlo SempiJesus Serrano-GuerreroPrakash ShenoyMarek SikoraAndrzej SkowronDamjan SkuljDominik SlezakZdenko SonickiLuca SpadaAnna StachowiakIvana Stajner-PapugaDaniel StamateLuciano StefaniniJaroslaw StepaniukMartin StepnickaMarcin SzczukaMiquel Sanchez-MarreMarco Elio Tabacchi

Settimo TerminiVicenc TorraJoan TorrensKrzysztof TrawinskiGracian TrivinoAlessandra TrunfioMayumi UedaZiya UlukanAlp UstundagIrem Ucal SarıLionel ValetAida VallsArthur Van CampLinda Van Der GaagBarbara VantaggiJirina VejnarovaThomas VetterleinMaria-Amparo VilaDoretta VivonaMarcin WolskiYu-Lung WuSlawomir ZadroznyCalogero ZarbaPawel ZielinskiMichele Zito

Table of Contents – Part I

Fuzzy Machine Learning and On-Line Modeling

Dynamic Quantification of Process Parameters in Viscose Productionwith Evolving Fuzzy Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Carlos Cernuda, Edwin Lughofer, Lisbeth Suppan, Thomas Roder,Roman Schmuck, Peter Hintenaus, Wolfgang Marzinger, andJurgen Kasberger

Statistical Dynamic Classification to Detect Changes in TemperatureTime Series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Laurent Hartert, Danielle Nuzillard, Jean-Louis Nicolas, andJean-Philippe Jeannot

A Possibilistic Rule-Based Classifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21Myriam Bounhas, Henri Prade, Mathieu Serrurier, andKhaled Mellouli

Uncertainty and Trust Estimation in Incrementally Learning FunctionApproximation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Andreas Buschermohle, Jan Schoenke, and Werner Brockmann

On the VC-Dimension of the Choquet Integral . . . . . . . . . . . . . . . . . . . . . . . 42Eyke Hullermeier and Ali Fallah Tehrani

A Fuzzy Residuated Approach to Case-Based Reasoning . . . . . . . . . . . . . . 51Sandra Sandri

A Unifying Framework for Classification Procedures Based on ClusterAggregation by Choquet Integral . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

Luigi Troiano

Balancing Interpretability against Accuracy in Fuzzy Modelingby Means of ACO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

Pablo Carmona, Juan Luis Castro, and Jose Luis Herrero

Towards a Fuzzy Extension of the Lopez de Mantaras Distance . . . . . . . . 81Eva Armengol, Pilar Dellunde, and Angel Garcıa-Cerdana

Optimal Piecewise Bilinear Modeling of Nonlinear Systems . . . . . . . . . . . . 91Luka Eciolaza and Michio Sugeno

Precise Vehicle Cruise Control System Based on On-Line Fuzzy ControlLearning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

Enrique Onieva, Jorge Godoy, and Jorge Villagra

XII Table of Contents – Part I

Robust Stabilization of Nonlinear Systems Modeled with PiecewiseBilinear Systems Based on Feedback Linearization . . . . . . . . . . . . . . . . . . . 111

Tadanari Taniguchi and Michio Sugeno

Computing with Words and Decision Making

Modeling Environmental Syndromes with Distinct Decision Attitudes . . . 121Gloria Bordogna, Mirco Boschetti, Pietro A. Brivio, Paola Carrara,Daniela Stroppiana, and C.J. Weissteiner

On the Applicability of Multi-Criteria Decision Making Techniquesin Fuzzy Querying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130

Guy De Tre, Jozo Dujmovic, Antoon Bronselaer, and Tom Matthe

Fuzzy Numbers as Utilities of Decision Making in Treatmentof Radiation Cystitis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140

Elisabeth Rakus-Andersson and Janusz Frey

A Linguistic Approach to Structural Analysis in Prospective Studies . . . . 150Pablo J. Villacorta, Antonio D. Masegosa,Dagoberto Castellanos, and Maria T. Lamata

A Fuzzy Group Decision Support System for Projects Evaluation . . . . . . 160Fahimeh Ramezani and Jie Lu

Risk Prediction Framework and Model for Bank External FundAttrition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170

Hua Lin and Guangquan Zhang

Group Decision Making with Comparative Linguistic Terms . . . . . . . . . . . 181Rosa M. Rodrıguez, Luis Martınez, and Francisco Herrera

An Extended Version of the Fuzzy Multicriteria Group Decision-MakingMethod in Evaluation Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191

Macarena Espinilla, Jie Lu, Jun Ma, and Luis Martınez

On Some Connections between Multidistances and Valued m-aryAdjacency Relations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201

Matteo Brunelli, Mario Fedrizzi, Michele Fedrizzi, andFranco Molinari

Logical Proportions – Further Investigations . . . . . . . . . . . . . . . . . . . . . . . . . 208Henri Prade and Gilles Richard

Duality between Addition and Removal: A Tool for Studying Changein Argumentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219

Pierre Bisquert, Claudette Cayrol, Florence Dupin de Saint-Cyr,and Marie-Christine Lagasquie-Schiex

Table of Contents – Part I XIII

Soft Computing in Computer Vision

F 1-transform Edge Detector Inspired by Canny’s Algorithm . . . . . . . . . . . 230Irina Perfilieva, Petra Hodakova, and Petr Hurtık

Coordinate-Based Pattern-Mining on Functional NeuroimagingDatabases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240

Julian Caspers, Karl Zilles, Simon B. Eickhoff, and Christoph Beierle

WAPSI: Web Application for Plant Species Identification Using FuzzyImage Retrieval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 250

Carlos Caballero and M. Carmen Aranda

Comparing the Efficiency of a Fuzzy Single-Stroke Character Recognizerwith Various Parameter Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 260

Alex Tormasi and Laszlo T. Koczy

Rough Sets and Complex Data Analysis:Theory and Applications

An Empirical Comparison of Rule Induction Using Feature Selectionwith the LEM2 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 270

Jerzy W. Grzymala-Busse

Management of Information Incompleteness in Rough Non-deterministicInformation Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 280

Hiroshi Sakai, Michinori Nakata, and Dominik Sl ↪ezak

Rough-Granular Computing Based Relational Data Mining . . . . . . . . . . . . 290Piotr Honko

On Different Ways of Handling Inconsistencies in Ordinal Classificationwith Monotonicity Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 300

Jerzy B�laszczynski, Weibin Deng, Feng Hu, Roman S�lowinski,Marcin Szel ↪ag, and Guoyin Wang

Fuzzy-Rough MRMS Method for Relevant and Significant AttributeSelection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 310

Pradipta Maji and Partha Garai

Rough Derivatives as Dynamic Granules in Rough Granular Calculus . . . 321Andrzej Skowron, Jaros�law Stepaniuk, Andrzej Jankowski, andJan G. Bazan

A Rough Set Approach to Knowledge Discovery by RelationApproximation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331

Sinh Hoa Nguyen and Hung Son Nguyen

XIV Table of Contents – Part I

Intelligent Databases and Information System

On Fuzzy Preference Queries Explicitly Handling Satisfaction Levels . . . . 341Olivier Pivert and Gregory Smits

On a Reinforced Fuzzy Inclusion and Its Application to DatabaseQuerying . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351

Patrick Bosc and Olivier Pivert

Implementable Representations of Level-2 Fuzzy Regions for Usein Databases and GIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361

Jorg Verstraete

Distinct Interpretations of Importance Query Weights in the Vectorp-norm Database Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371

Gloria Bordogna, Alberto Marcellini, and Giuseppe Psaila

On Link Validity in Bibliographic Knowledge Bases . . . . . . . . . . . . . . . . . . 380Madalina Croitoru, Lea Guizol, and Michel Leclere

Text Retrieval and Visualization in Databases Using Tag Clouds . . . . . . . 390Ursula Torres-Parejo, Jesus Roque Campana,Maria-Amparo Vila, and Miguel Delgado

A Bipolar Approach to the Handling of User Preferences in BusinessProcesses Retrieval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 400

Katia Abbaci, Fernando Lemos, Allel Hadjali, Daniela Grigori,Ludovic Lietard, Daniel Rocacher, and Mokrane Bouzeghoub

Evaluating Possibilistic Valid-Time Queries . . . . . . . . . . . . . . . . . . . . . . . . . . 410Christophe Billiet, Jose Enrique Pons, Olga Pons Capote, andGuy De Tre

A Possibilistic Valid-Time Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 420Jose Enrique Pons, Christophe Billiet, Olga Pons Capote, andGuy De Tre

Fuzzy Ontologies for Specialized Knowledge Representationin WordNet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 430

Fernando Bobillo, Juan Gomez-Romero, and Pilar Leon Arauz

Individual Link Model for Text Classification . . . . . . . . . . . . . . . . . . . . . . . . 440Nam Do-Hoang Le, Thai-Son Tran, and Minh-Triet Tran

Coreference Detection of Low Quality Objects . . . . . . . . . . . . . . . . . . . . . . . 450Joachim Nielandt, Antoon Bronselaer, and Guy De Tre

Information Retrieval: Ranking Results According to CalendarCriteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 460

Delphine Battistelli, Marcel Cori, Jean-Luc Minel, andCharles Teissedre

Table of Contents – Part I XV

Towards an Efficient Processing of Outranking-Based PreferenceQueries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 471

Olivier Pivert and Gregory Smits

Information Fusion Systems

Robustness of Multiset Merge Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 481Antoon Bronselaer, Daan Van Britsom, and Guy De Tre

Semantic Beliefs Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 491Amandine Bellenger, Xavier Lerouvreur, Habib Abdulrab, andJean-Philippe Kotowicz

Weighted Fuzzy Aggregation for Metasearch: An Applicationof Choquet Integral . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 501

Arijit De, Elizabeth D. Diaz, and Vijay V. Raghavan

Encoding Preference Queries to an Uncertain Database in PossibilisticAnswer Set Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 511

Roberto Confalonieri and Henri Prade

A Multi Level Evaluation for Fusion System InteractionImprovement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 521

Abdellah Lamallem, Lionel Valet, and Didier Coquin

Choquet Integral Parameter Optimization for a Fusion System Devotedto Image Interpretation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 531

Marcelo Beckmann, Lionel Valet, and Beatriz S.L.P. De Lima

Philosophical and Methodological Aspects of SoftComputing

Fuzzy Sets and Systems before the Fuzzy Boom . . . . . . . . . . . . . . . . . . . . . . 541Rudolf Seising

A New Characterization for n–Fold Positive Implicative BL–Logics . . . . . 552Esko Turunen, Nganteu Tchikapa, and Celestin Lele

A Pairwise Distance View of Cluster Validity . . . . . . . . . . . . . . . . . . . . . . . . 561Mohammad Rawashdeh and Anca Ralescu

On Modal Operators in �Lukasiewicz’ n-Valued Logics . . . . . . . . . . . . . . . . . 571Jorma K. Mattila

Basic Issues in Rough Sets

Associated Near Sets of Merotopies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 586James F. Peters and Sheela Ramanna

XVI Table of Contents – Part I

Roughness in Residuated Lattices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 596Jirı Rachunek and Dana Salounova

A General Set Theoretic Approximation Framework . . . . . . . . . . . . . . . . . . 604Zoltan Csajbok and Tamas Mihalydeak

Label Ranking: A New Rule-Based Label Ranking Method . . . . . . . . . . . . 613Massimo Gurrieri, Xavier Siebert, Philippe Fortemps,Salvatore Greco, and Roman S�lowinski

Distinguishing Vagueness from Ambiguity by Meansof Pawlak-Brouwer-Zadeh Lattices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 624

Salvatore Greco, Benedetto Matarazzo, and Roman S�lowinski

Relationships between Connectives in Three-Valued Logics . . . . . . . . . . . . 633Davide Ciucci and Didier Dubois

Ill-known Set Approach to Disjunctive Variables: Calculationsof Graded Ill-Known Intervals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 643

Masahiro Inuiguchi

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 653

Group Decision Making with Comparative

Linguistic Terms

Rosa M. Rodrıguez1, Luis Martınez1, and Francisco Herrera2

1 University of Jaen, Computer Science Department23071, Jaen, Spain

{rmrodrig,martin}@ujaen.es2 University of Granada, Computer Science and A.I. Department

18071, Granada, Spainherrera@decsai.ugr.es

Abstract. In group decision making (GDM) framework, we focus ondecision problems defined under uncertainty where decision makers canhesitate among several values to elicit their preferences. In such cases,the use of hesitant fuzzy linguistic term sets (HFLTS) can facilitate theelicitation of decision makers preferences. In this contribution, our aimis to propose a linguistic GDM model that allows to decision makersuse single linguistic terms or comparative linguistic terms to expresstheir preferences and obtain the solution set of alternatives of the GDMproblem.

Keywords: Group decision making, hesitant fuzzy linguistic term sets,comparative linguistic terms, context-free grammar.

1 Introduction

Decision making is a usual process for human beings and companies in differentareas such as, engineering [10], planning [20], etc. In decision making problemswith multiple experts, each expert expresses his/her preferences depending onthe nature of the alternatives and on his/her own knowledge over them. Usu-ally, this knowledge is vague and imprecise. In such cases, the fuzzy logic [8]and fuzzy linguistic approach [18] provide suitable tools to deal with this typeof uncertainty. The use of linguistic information implies to carry out processesof computing with words (CWW) [11,19]. There are different linguistic com-puting models to accomplish such processes [5,9,15]. However, such approachesare limited to model qualitative settings where decision makers hesitate amongdifferent values, because they are thinking of several linguistic terms to providetheir preferences.

Torra introduced the concept of hesitant fuzzy sets [14] to manage situationsin quantitative settings, when decision makers hesitate among different valuesto determine the membership of an element into a set. In qualitative settings itmay occur a similar situation, decision makers hesitate among different linguistic

S. Greco et al. (Eds.): IPMU 2012, Part I, CCIS 297, pp. 181–190, 2012.c© Springer-Verlag Berlin Heidelberg 2012

182 R.M. Rodrıguez, L. Martınez, and F. Herrera

terms. Rodrıguez et al. proposed the concept of HFLTS [12] to facilitate theelicitation of such linguistic information by comparative linguistic terms.

The aim of this contribution is to develop a linguistic GDM model capableto manage hesitant information by means of comparative linguistic terms repre-sented by HFLTS. These comparative terms facilitate the elicitation of linguisticinformation to decision makers in hesitant situations. The proposed GDM modelwill manage this type of information by using linguistic intervals.

This paper is structured as follows: Section 2, introduces a basic scheme of aGDM problem and makes a brief review about fuzzy linguistic approach. Section3, revises the elicitation of comparative linguistic terms represented by HFLTS.Section 4, presents a linguistic GDM model that deals with comparative lin-guistic terms. Section 5 shows an illustrative example of a GDM problem, andfinally, Section 6 points out some concluding remarks.

2 Preliminaries

This section introduces a basic scheme for a GDM problem and reviews the fuzzylinguistic approach basis of the HFLTS.

2.1 Scheme of a Group Decision Making Problem

A GDM problem is defined as a decision situation where a finite set of experts,E = {e1, . . . , em} (m >= 2), express their preferences over a finite set of alter-natives, X = {x1, . . . , xn}, (n >= 2) to obtain a solution set of alternatives forthe decision problem [7]. Usually, each expert, ek, provides her/his preferenceson X by means of a preference relation P k, μPk : X ×X −→ D,

P k =

⎛⎜⎝

pk11 . . . pk1n...

. . ....

pkn1 . . . pknn

⎞⎟⎠

where each assessment, μPk(xi, xj) = pkij , represents the degree of preference ofthe alternative xi over xj according to expert ek.

Usually, GDM problems have been solved performing a selection process whereexperts obtain the best alternative from their preferences [13]. The selectionprocess consists of two phases (see Fig.1).

– Aggregation phase: the experts preferences are aggregated to obtain a collec-tive preference matrix that reflects the preferences provided by all experts.

– Exploitation phase: it selects the best alternative/s to solve the decisionproblem by ranking the collective preferences obtained in the previous phaseby using a choice function [3].

Group Decision Making with Comparative Linguistic Terms 183

Fig. 1. General schema of a group decision making problem

2.2 Fuzzy Linguistic Approach

The fuzzy linguistic approach [18] represents qualitative settings by means oflinguistic variables. The concept of linguistic variable was introduced by Zadeh[18] as “a variable whose values are not numbers but words or sentences in anatural or artificial language”. To model linguistically the information is neces-sary to choose the appropriate linguistic descriptors for the linguistic term setand their semantics. To do so, there are different possibilities [16]. We will useone of them that consists of applying directly the term set by considering all theterms distributed on a scale that has an order defined [16]. In these cases, it isrequired that in the linguistic term set there are the following operators:

1. Negation: Neg(si) = sj with j = g-i (g+1 is the granularity of the term set).2. Maximization: Max(si, sj) = si if si ≥ sj.3. Minimization: Min(si, sj) = si if si ≤ sj .

The semantics of the terms is represented by fuzzy numbers defined in the in-terval [0,1], described by membership functions [1].

We aforementioned that the use of linguistic information implies processes ofCWW. To perform these computations in the fuzzy linguistic approach appearedtwo classical computational models:

– Semantic model that computes with linguistic terms by means of operationsassociated to their membership functions based on the Extension Principle[2].

– Symbolic model that uses the ordered structure of the linguistic terms tooperate [16].

Symbolic models have been widely used in decision making because of theirsimplicity and understandability. In this contribution, we will use a symbolicmodel in the proposal for the GDM model.

3 Elicitation of Comparative Linguistic Terms

Our interest is focused on GDM problems under uncertainty where decisionmakers may hesitate among different values to assess qualitative settings. To

184 R.M. Rodrıguez, L. Martınez, and F. Herrera

manage such a situation, we propose the use of comparative linguistic termsrepresented by HFLTS. In [12] Rodrıguez et al. defined the following context-free grammar to generate expressions with comparative linguistic terms.

Definition 1. [12] Let GH be a context-free grammar and S = {s0, . . . , sg} alinguistic term set. The elements of GH = (VN , VT , I, P ) are defined as follows:VN = {〈primary term〉, 〈composite term〉, 〈unary relation〉, 〈binary relation〉,

〈conjunction〉}VT = {lower than, greater than, between, and, s0, s1, . . . , sg}I ∈ VN

The production rules are defined in an extended Backus Naur Form so that thebrackets enclose optional elements and the symbol | indicates alternative elements[1]. For the context-free grammar, GH , the production rules are the following:

P = {I ::= 〈primary term〉|〈composite term〉〈composite term〉 ::= 〈unary relation〉〈primary term〉|〈binary relation〉

〈primary term〉|〈conjunction〉〈primary term〉〈primary term〉 ::= s0|s1| . . . |sg〈unary relation〉 ::= lower than|greater than〈binary relation〉 ::= between〈conjunction〉 ::= and}

These linguistic expressions are represented by HFLTS.

Definition 2. [12] An HFLTS, HS, is an ordered finite subset of consecutivelinguistic terms of S, where S = {s0, . . . , sg} is a linguistic term set.

For example, letS = {nothing, very low, low,medium, high, very high, perfect}be a linguistic term set and X an alternative, an HFLTS might be:

HS(X) = {high, very high, perfect}To obtain HFLTS from the comparative linguistic terms generated by the context-free grammar GH , was defined the transformation function EGH .

Definition 3. [12] Let EGH be a function that transforms linguistic expressions,ll, obtained by GH , into HFLTS, HS, where S is the linguistic term set used byGH .

EGH : Sll −→ HS (1)

In decision making is often to carry out comparisons between values. The com-parison between two HFLTS is complex, because an HFLTS is a set of linguisticterms. Therefore, to compare two HFLTS was introduced the concept of envelopeof an HFLTS.

Definition 4. [12] The envelope of a HFLTS, env(HS), is a linguistic intervalwhose limits are obtained by means of upper bound (max) and lower bound(min):

env(HS) = [HS− , HS+ ], HS− ≤ HS+ (2)

Group Decision Making with Comparative Linguistic Terms 185

whereHS+ = max(si) = sj, si ∈ HS and si ≤ sj ∀i andHS− = min(si) = sj, si ∈ HS and si ≥ sj ∀iFollowing the previous example, HS(X) = {high, very high, perfect}, its en-

velope is:

env(HS) = [high, perfect]

Once obtained the envelopes of HFLTS, the comparison is conducted by intervalvalues. Different approaches can be applied to carry out such comparison [12].More operations with HFLTS and properties can be found in [12].

4 Linguistic Group Decision Making Model Dealing withComparative Linguistic Terms

The aim of this contribution is to propose a linguistic GDM model that copeswith hesitant situations in qualitative settings in which decision makers providelinguistic information by means of single linguistic terms or comparative linguis-tic terms. This model based on the classical symbolic model uses the indexes ofthe linguistic term set to operate across the decision making process. It extendsthe decision resolution scheme shown in Fig. 1 adding a phase to manage lin-guistic information by means of HFLTS. It consists mainly of three phases (seeFig. 2):

Fig. 2. Scheme of the linguistic group decision making model

1. Transformation of the comparative linguistic terms preference relations intoHFLTSExperts provide their preference relation, P k, by using single linguistic termsor comparative linguistic terms, μPk : X ×X −→ Sll,

P k =

⎛⎜⎝

pk11 . . . pk1n...

. . ....

pkn1 . . . pknn

⎞⎟⎠

186 R.M. Rodrıguez, L. Martınez, and F. Herrera

where each assessment pkij ∈ Sll, represents the preference degree of the al-ternative xi over xj according to expert ek, expressed in the informationdomain Sll. To solve the GDM problem the comparative linguistic termsare transformed into HFLTS by means of the transformation function EGH .Afterwards, it is computed an envelope for each HFLTS that obtains a lin-guistic interval that will be used to aggregate the preferences provided byexperts, env(HS(p

kij)) = [pk−ij , pk+ij ],

P k =

⎛⎜⎝

[pk−11 , pk+11

]. . .

[pk−1n , p

k+1n

]...

. . ....[

pk−n1 , pk+n1

]. . .

[pk−nn , pk+nn

]

⎞⎟⎠

2. Aggregation of the preference relations represented by linguistic intervalsThe linguistic intervals are aggregated to obtain a collective preference rela-tion PC . We use the LOWA aggregation operator [4] to aggregate the rightlimits, pk+ij , and the left limits, pk−ij of the intervals.

PC =

⎛⎜⎝

[p−11, p

+11

]. . .

[p−1n, p

+1n

]...

. . ....[

p−n1, p+n1

]. . . [p−nn, p

+nn]

⎞⎟⎠

where i, j ∈ {1, . . . , n} and n is the number of alternatives.3. Exploitation phase

Once the linguistic intervals have been aggregated, the set of alternatives isordered to select the best one/s. To do so, we use the approach proposed byJiang [6] that deals with interval preference relations and obtains a ranking ofalternatives based on numerical possibility degrees according to the followingsteps:

(a) Firstly, it is calculated the mean preference relation PC = (pij)n×n, andthe error matrix δ = (δij)n×n, that represents the mean distance of thelimits of the intervals of PC ,

pij =1

2(p−ij + p+ij) (3)

δij =1

2(p+ij − p−ij) (4)

where i, j ∈ {1, 2, . . . , n}

Remark 1. We note that to deal with linguistic intervals symbolically,these functions are adapted, so pij = 1

2 (ind(p−ij) + ind(p+ij)), δij =

12 (ind(p

+ij)− ind(p−ij)); ind(si) = i.

(b) Afterwards, it is used the error propagation principle [17] to obtain thepriority vector w = (w1, . . . , wn) of the mean preference relation, PC .

Group Decision Making with Comparative Linguistic Terms 187

wi =(∑n

j=1 pij +n2 − 1)

n(n− 1)i = 1, 2, . . . , n (5)

It is calculated an error vector Λw = (Λw1, . . . , Λwn) of w due to theimprecise values of pij , by using the following function.

Λwi =1

n(n− 1)

√√√√n∑

j=1

δ2ij , i = 1, 2, . . . , n (6)

And thus it is got the priority vector w = (w1, . . . , wn)T of the collective

matrix, PC , where wi = [wi − Λwi, wi + Λwi], i = 1, . . . , n.(c) To rank these interval weights wi(i = 1, . . . , n), each wi is compared

with all wi by using the possibility degree function, and it is then builta possibility degree matrix PD = (pdij)n×n.

pdij = p(wi ≥ wj) =min(2(Λwi + Λwj),max(wi + Λwi − (wj − Λwj), 0))

2(Λwi + Λwj)(7)

A non-dominance choice degree is applied to the possibility degrees toobtain the solution set of alternatives. To do so, the possibility degreesof the alternatives pdij , are summed by rows, and they are ranked in adescending order.

pdi =

n∑j=1

pdij i = 1, . . . , n (8)

Finally, the alternatives are ordered according to pdi and then the bestalternative is selected.

5 Illustrative Example

Here, we present a GDM problem solved by the proposed GDM model.Let a GDM problem be defined in qualitative settings where a set of ex-

perts, E = {e1, e2, e3}, provide their preferences over a set of alternatives,X = {x1, x2, x3, x4}. Experts provide their preferences by using the comparativelinguistic terms generated by the context-free grammar GH , (see Def. 1). Suchlinguistic expressions are represented by HFLTS. The linguistic term set used forthe context-free grammar is S = {nothing(n), very low(vl), low(l),medium(m),high(h), very high(vh), perfect(p)} and the preference relations provided by theexperts are the following ones:

P1=

⎛⎜⎝

− less than vl vh more than hmore than vl − between h and vh less than m

l less than h − more than vhless than vh more than h less than m −

⎞⎟⎠

188 R.M. Rodrıguez, L. Martınez, and F. Herrera

P2=

⎛⎜⎝

− less than m more than h between vl and lmore than h − h vlless than vh l − more than vhless than l vh between vh and p −

⎞⎟⎠

P 3 =

⎛⎜⎝

− more than h between vl and l hless than vh − more than m more than hless than l less than vh − vh

l less than vh vl −

⎞⎟⎠

According to the Fig. 2, the GDM process consists of:

1. Transformation of the comparative linguistic terms preference relations intoHFLTSThe linguistic preference relations provided by the experts are transformedinto HFLTS by means of the transformation function EGH :

P 1 =

⎛⎜⎝

− {n, vl} {vh} {h, vh, p}{vl, l,m, h, vh, p} − {h, vh} {n, vl, l,m}

{l} {n, vl, l, m, h} − {vh, p}{n, vl, l,m, h, vh} {h, vh, p} {n, vl, l,m} −

⎞⎟⎠

P 2 =

⎛⎜⎝

− {n, vl, l,m} {h, vh, p} {vl, l}{h, vh, p} − {h} {vl}

{n, vl, l, m, h, vh} {l} − {vh, p}{n, vl, l} {vh} {vh, p} −

⎞⎟⎠

P 3 =

⎛⎜⎝

− {h, vh, p} {vl, l} {h}{n, vl, l,m, h, vh} − {m,h, vh, p} {h, vh, p}

{n, vl, l} {n, vl, l, m, h, vh} − {vh}{l} {n, vl, l, m, h, vh} {vl} −

⎞⎟⎠

The envelopes obtained for each HFLTS are the following ones:

P 1 =

⎛⎜⎝

− [n, vl] [vh, vh] [h, p][vl,p] − [h, vh] [n,m][l,l] [n, h] − [vh, p]

[n,vh] [h, p] [n,m] −

⎞⎟⎠P 2 =

⎛⎜⎝

− [n,m] [h, p] [vl, l][h,p] − [h, h] [vl, vl][n,vh] [l, l] − [vh, p][n,l] [vh, vh] [vh, p] −

⎞⎟⎠

P3=

⎛⎜⎝

− [h, p] [vl, l] [h, h][n,vh] − [m, p] [h, p][n,l] [n, vh] − [vh, vh][l,l] [n, vh] [vl, vl] −

⎞⎟⎠

2. Aggregation of the preference relations represented by linguistic intervalsThe linguistic intervals are aggregated by using the LOWA operator to obtainthe collective preferences matrix,

PC =

⎛⎜⎝

− [vl,m] [h, vh] [m, vh][l,p] − [h, p] [m,h][vl,h] [vl, h] − [vh, p][vl,m] [h, p] [l,m] −

⎞⎟⎠

Group Decision Making with Comparative Linguistic Terms 189

3. Exploitation phaseOnce obtained the collective preferences from experts, it is used the approachproposed by Jiang [6] to obtain the solution set of alternatives.a) Mean preference relation PC , and error-matrix δ, of the collective pref-

erence relation PC :

PC =

⎛⎜⎝

− 2 4.5 44 − 5 3.52.5 2.5 − 5.52 5 2.5 −

⎞⎟⎠ δ =

⎛⎜⎝

− 1 0.5 12 − 1 0.51.5 1.5 − 0.51 1 0.5 −

⎞⎟⎠

b) Priority vector w, and error vector Λw:

w = (0.958, 1.125, 0.958, 0.875)

Λw = (0.125, 0.191, 0.182, 0.125)

c) Possibility degree matrix PD:

PD =

⎛⎜⎝

− 0.236 0.5 0.6670.764 − 0.723 0.8950.5 0.276 − 0.635

0.333 0.104 0.364 −

⎞⎟⎠

d) Finally a dominance choice degree is applied over the possibility degreeof the alternatives

pd1 = 1.403 pd2 = 2.382 pd3 = 1.411 pd4 = 0.801

and then the ranking of the alternatives is:

x2 > x3 > x1 > x4,

being the best alternative of the GDM problem, x2.

6 Conclusions

GDM is a key area in many different fields such that decision makers may facesituations in which they hesitate among several linguistic terms to provide theirpreferences. In this contribution, we have presented a linguistic GDM model ca-pable to deal with HFLTS, that facilitates the elicitation of hesitant informationto decision makers.

Acknowledgments. This work is partially supported by the Research ProjectTIN-2009-08286 and FEDER funds.

References

1. Bordogna, G., Pasi, G.: A fuzzy linguistic approach generalizing boolean informa-tion retrieval: A model and its evaluation. Journal of the American Society forInformation Science 44, 70–82 (1993)

190 R.M. Rodrıguez, L. Martınez, and F. Herrera

2. Degani, R., Bortolan, G.: The problem of linguistic approximation in clinical deci-sion making. Int. Journal of Approximate Reasoning 2, 143–162 (1988)

3. Herrera, F., Herrera-Viedma, E.: Choice functions and mechanisms for linguisticpreference relations. European J. of Operational Research 120(1), 144–161 (2000)

4. Herrera, F., Herrera-Viedma, E., Verdegay, J.L.: A sequential selection processin group decision making with a linguistic assessment approach. Information Sci-ences 85(4), 223–239 (1995)

5. Herrera, F., Martınez, L.: A 2-tuple fuzzy linguistic representation model for com-puting with words. IEEE Transactions on Fuzzy Systems 8(6), 746–752 (2000)

6. Jiang, Y.: An approach to group decision making based on interval fuzzy preferencerelations. J. of Systems Science and Systems Engineering 16(1), 113–120 (2007)

7. Kacprzyk, J.: Group decision making with a fuzzy linguistic majority. Fuzzy Setsand Systems 18, 105–118 (1986)

8. Klir, G.J., Yuan, B.: Fuzzy sets and fuzzy logic: Theory and Applications. Prentice-Hall PTR (1995)

9. Lawry, J.: A methodology for computing with words. International Journal of Ap-proximate Reasoning 28, 51–89 (2001)

10. Liu, J., Martınez, L., Wang, H., Rodrıguez, R.M., Novozhilov, V.: Computing withwords in risk assessment. International Journal of Computational Intelligence Sys-tems 3(4), 396–419 (2010)

11. Martınez, L., Ruan, D., Herrera, F.: Computing with words in decision supportsystems: An overview on models and applications. International Journal of Com-putational Intelligence Systems 3(4), 382–395 (2010)

12. Rodrıguez, R.M., Martınez, L., Herrera, F.: Hesitant fuzzy linguistic term sets fordecision making. IEEE Transactions on Fuzzy Systems 20(1), 109–119 (2012)

13. Roubens, M.: Fuzzy sets and decision analysis. Fuzzy Sets and Systems 90, 199–206(1997)

14. Torra, V.: Hesitant fuzzy sets. Int. J. of Intelligent Systems 25(6), 529–539 (2010)15. Turksen, I.B.: Type 2 representation and reasoning for CWW. Fuzzy Sets and

Systems 127, 17–36 (2002)16. Yager, R.R.: An approach to ordinal decision making. International Journal of

Approximate Reasoning 12(3-4), 237–261 (1995)17. Yoon, K.: The propagation of errors in multiple-attribute decision analysis: a prac-

tical approach. Journal of the Operational Research Society 40, 681–686 (1989)18. Zadeh, L.: The concept of a linguistic variable and its applications to approxi-

mate reasoning. Information Sciences, Part I, II, III (8,9), 199–249, 301–357, 43–80(1975)

19. Zadeh, L.: Fuzzy logic = computing with words. IEEE Transactions on FuzzySystems 94(2), 103–111 (1996)

20. Zhang, T., Zhang, G., Ma, J., Lu, J.: Power distribution system planning evaluationby a fuzzy multi-criteria group decision support system. International Journal ofComputational Intelligence Systems 3(4), 474–485 (2010)

Author Index

Abad, Miguel Angel II-560Abbaci, Katia I-400Abbasbandy, Saeid III-79Abdulrab, Habib I-491Abid, Mohamed III-39Ahmad, Khurshid III-379Ah-Pine, Julien IV-238Aiche, Farid III-9Alcalde, Cristina II-305Almeida, Rui Jorge III-554Amgoud, Leila III-122, IV-480Ammar, Asma III-596Amor, Nahla Ben III-470Anderson, Terry II-265Angilella, Silvia IV-248Antonucci, Alessandro III-491Anzilli, Luca IV-44, IV-54Aranda, M. Carmen I-250Argentini, Andrea III-511Armengol, Eva I-81Ayachi, Raouia III-470

Baczynski, Micha�l II-375, II-501Baglieri, Daniela IV-607Baioletti, Marco III-211Ballini, Rosangela IV-20Ban, Adrian I. III-29, III-49Bas, Esra IV-393Battistelli, Delphine I-460Bazan, Jan G. I-321, II-550Bazan-Socha, Stanislawa II-550Beckmann, Marcelo I-531Bedregal, Benjamın II-450, III-326Beierle, Christoph I-240, IV-665Beliakov, Gleb III-326Bell, David III-543Bellenger, Amandine I-491Benferhat, Salem III-470, III-585Ben Mrad, Ali III-39Ben Yaghlane, Boutheina III-481,

III-575Bereketli, Ilke IV-347Bertens, Roel III-161Berti, Patrizia IV-114

Bertomeu Castello, Nuria IV-328Bi, Yaxin II-265, III-564Biazzo, Veronica IV-146Bica, Alexandru A. III-29Billiet, Christophe I-410, I-420Bisquert, Pierre I-219Blanzieri, Enrico III-511B�laszczynski, Jerzy I-300Bobillo, Fernando I-430Bodjanova, Slavka III-296Bordogna, Gloria I-121, I-371Borkowski, Janusz II-570Borras, Joan II-127Bortot, Silvia IV-188Bosc, Patrick I-351Boschetti, Mirco I-121Boukhris, Imen III-585Bounhas, Myriam I-21Bouzeghoub, Mokrane I-400Bozhenyuk, Alexander II-98Brivio, Pietro A. I-121Brockmann, Werner I-32, III-231Bronselaer, Antoon I-130, I-450, I-481,

II-276Browne, Fiona III-543Brunelli, Matteo I-201Buregwa-Czuma, Sylwia II-550Burns, Nicola II-265Burusco, Ana II-305Buschermohle, Andreas I-32Bustince, Humberto II-450, III-326

Caballero, Carlos I-250Calvo, Tomasa IV-549Campana, Jesus Roque I-390Capotorti, Andrea IV-124Cardin, Marta IV-37Cardoso, Janette III-521Carfı, David IV-578, IV-593, IV-607,

IV-642Carlsson, Christer III-19Carmona, Pablo I-71Carrara, Paola I-121Caspers, Julian I-240

654 Author Index

Castellani, Gilberto IV-134Castellanos, Dagoberto I-150Castillo-Ortega, Rita II-285Castro, Juan Luis I-71, II-245Cattaneo, Marco E.G.V. III-491Cavallo, Bice IV-315Cayrol, Claudette I-219Cerami, Marco II-235Cernuda, Carlos I-1Cetin Demirel, Nihan IV-423Cevikcan, Emre IV-354Chammas, Ghassan IV-94Chebbah, Mouna III-575Cholvy, Laurence III-501Ciucci, Davide I-633Coletti, Giulianella III-211, IV-168Colla, Valentina II-78Confalonieri, Roberto I-511, II-88Conti, Dante II-137Coquin, Didier I-521Corani, Giorgio III-491Cori, Marcel I-460Coroianu, Lucian III-29, III-49Corrente, Salvatore IV-248, IV-469Costa, Anna Helena Reali II-107Couceiro, Miguel III-347, IV-178Couso, Ines III-388Croitoru, Madalina I-380Cruz, Carlos III-102Csajbok, Zoltan I-604Cuzzocrea, Alfredo II-580

Dagnino, Giovanni Battista IV-607Daniel, Milan III-532D’Apuzzo, Livia IV-315da Silva, Valdinei Freire II-107De, Arijit I-501De Baets, Bernard II-171, IV-286,

IV-296, IV-306De Bock, Jasper III-400de Cooman, Gert III-400, III-430,

III-440, III-460De Felice, Massimo IV-134Delcroix, Veronique III-39Delgado, Miguel I-390Delgado, Myriam R. IV-655De Lima, Beatriz S.L.P. I-531Dellunde, Pilar I-81De Loof, Karel IV-296de Melo, Leonardo G. IV-655

De Meyer, Hans II-171, IV-296, IV-306Demirel, Tufan IV-432Deng, Weibin I-300Denoeux, Thierry III-554de Saint-Cyr, Florence Dupin I-219Deschrijver, Glad II-471De Smet, Yves IV-338, IV-383De Tre, Guy I-130, I-410, I-420, I-450,

I-481, II-276, II-461De Virgilio, Roberto II-539Diaconescu, Denisa II-194Diaz, Elizabeth D. I-501Dıaz, Irene IV-499, IV-509Dıaz, Juan Carlos II-395Dıaz, Susana IV-286Dick, Michael II-35, II-44Divari, Maria II-1, III-271, IV-539Drewniak, Jozef II-511Drygas, Pawe�l II-521Dubey, Dipti IV-458Dubois, Didier I-633, III-9, III-306,

III-347, III-388, III-521Dujmovic, Jozo I-130, III-336Durand, Nicolas III-410Dvorak, Antonın IV-208Dyczkowski, Krzysztof II-441

Eciolaza, Luka I-91Eickhoff, Simon B. I-240Elbers, Armin R. III-151Elouedi, Zied III-585, III-596, IV-373El-Zekey, Moataz II-216Ennaceur, Amel IV-373Eppe, Stefan IV-383Erol Genevois, Mujde IV-347, IV-413Espinilla, Macarena I-191Esteva, Francesc II-235Ezzati, Reza III-79

Facchinetti, Gisella IV-54Fallah Tehrani, Ali I-42Farion, Ken III-142Fedrizzi, Mario I-201Fedrizzi, Michele I-201, IV-30Fermuller, Christian G. IV-632Fernandez, Javier II-450, III-326Fersini, Elisabetta II-117Figueira, Jose Rui IV-469Finthammer, Marc IV-665Fisichella, Marco II-580

Author Index 655

Fortemps, Philippe I-613Franzoi, Laura III-1Freson, Steven IV-306Frey, Janusz I-140Fuentes-Gonzalez, Ramon II-305Fujita, Tomohiko IV-490Fuller, Robert III-19Funatomi, Takuya IV-490

Gagolewski, Marek III-276Garai, Partha I-310Garcıa-Cerdana, Angel I-81, II-235Gibert, Karina II-137Gilio, Angelo IV-146Giove, Silvio IV-37Godo, Lluis II-216Godoy, Jorge I-101Gomez, Daniel III-317Gomez-Romero, Juan I-430Gomide, Fernando IV-20Greco, Salvatore I-613, I-624, III-360,

IV-248, IV-469Grigori, Daniela I-400Grzegorzewski, Przemys�law II-335,

III-59Grzymala-Busse, Jerzy W. I-270Guerra, Maria Letizia IV-64Guillaume, Romain IV-104Guizol, Lea I-380Gurrieri, Massimo I-613

Hadjali, Allel I-400Hamed, Mohammad Ghasemi III-410Hartert, Laurent I-11Haun, Stefan II-35, II-44He, Yulin III-112Herencia, Jose A. II-4Herrera, Francisco I-181Herrero, Jose Luis I-71Higgins, Colm III-543Hintenaus, Peter I-1Hlinena, Dana II-345Hodakova, Petra I-230Holcapek, Michal IV-208Honko, Piotr I-290Hoppe, Anett II-35, II-44Hossein Zadeh, Parisa D. III-191Hu, Feng I-300Hu, Yanxing III-112Hullermeier, Eyke I-42

Hulsmann, Jens III-231Huntley, Nathan III-430Hurtık, Petr I-230

Iglesias, Tania III-356Inan, Hasier II-88Inthorn, Julia II-35, II-44Inuiguchi, Masahiro I-643Isern, David II-127

Jacob, Christelle III-521Janis, Vladimir II-491, III-356Jankowski, Andrzej I-321Jayaram, Balasubramaniam II-365,

II-385Jeannot, Jean-Philippe I-11Jenei, Sandor III-251Jin, Yan III-543Jirousek, Radim IV-676Jwaid, Tarad II-171

Kacprzyk, Janusz II-529Kagawa, Junko II-405Kahraman, Cengiz IV-441, IV-449Kakusho, Koh II-425Kalina, Martin II-345, III-296Kanenishi, Kazuhide II-405Kasberger, Jurgen I-1Kasperski, Adam IV-74Kaymak, Uzay III-554Kılıc, Huseyin Selcuk IV-354Kleiter, Gernot D. IV-157Klement, Erich Peter IV-559Koczy, Laszlo T. I-260Kolesarova, Anna IV-565Kop, Yesim IV-413Kotowicz, Jean-Philippe I-491Kral’, Pavol II-345Krol, Anna II-355Krupka, Michal III-171Kurpisz, Adam IV-74

Laamari, Wafa III-481Labreuche, Christophe IV-258Lagasquie-Schiex, Marie-Christine

I-219Lamallem, Abdellah I-521Lamata, Maria T. I-150Lastovicka, Jan III-171Lawry, Jonathan II-255

656 Author Index

Le, Nam Do-Hoang I-440Leclere, Michel I-380Lefevre, Eric IV-373Lele, Celestin I-552Lemos, Fernando I-400Leon Arauz, Pilar I-430Leporati, Alberto II-117Lerouvreur, Xavier I-491Leustean, Ioana II-226Lewicki, Arkadiusz III-241Li, Guanyi II-255Li, Jun IV-278, IV-565Liao, Jing III-564Lietard, Ludovic I-400Lin, Hua I-170Lingras, Pawan III-596Liu, Jame N.K. III-112Liu, Weiru III-543Loeffen, Willie L. III-151Lu, Jie I-160, I-191Lucas, Luıs Alberto IV-655Lughofer, Edwin I-1

Ma, Jun I-191Maalej, Mohamed Amine III-39Maciel, Leandro IV-20Magni, Carlo Alberto IV-64Mahmudov, Elimhan N. IV-364Maji, Pradipta I-310Mandal, Sayantan II-385Marcellini, Alberto I-371Marichal, Jean-Luc IV-178Marın, Nicolas II-285Marques Pereira, Ricardo Alberto

IV-188Martin, Arnaud III-575Martın, Javier IV-549Martinetti, Davide IV-286Martınez, Luis I-181, I-191Martınez, Sergio IV-519Marzinger, Wolfgang I-1Mas, M. III-286Masegosa, Antonio D. I-150Massanet, Sebastia II-315Masternak, Ryszard IV-665Matarazzo, Benedetto I-624Matthe, Tom I-130, II-461Mattila, Jorma K. I-571Mattioli, Gabriel III-261Mayag, Brice IV-238

Mayor, Gaspar IV-549Medina, Jesus II-395Mehra, Aparna IV-458Mellouli, Khaled I-21Menasalvas, Ernestina II-560Menendez, Carlos II-295Mesiar, Radko III-360, III-370, IV-278,

IV-559, IV-565Mesiarova-Zemankova, Andrea III-379Messina, Enza II-117Mezei, Jozsef III-19Michalowski, Wojtek III-142Miglionico, Maria Cristina IV-84Mihalydeak, Tamas I-604Milicchio, Franco II-539Minel, Jean-Luc I-460Minoh, Michihiko II-415, II-425, IV-490Miranda, Enrique III-440Mitsuhara, Hiroyuki II-405Molchanova, Olga II-434Molinari, Franco I-201Monserrat, M. III-286Montero, Javier III-317Montes, Ignacio II-491Montes, Susana II-491, III-356, IV-286Moreno, Antonio II-127Moreo, Alejandro II-245Moriconi, Franco IV-134Morimura, Yoshitaka IV-490Moriya, Katsuhiko II-405Mukunoki, Masayuki II-415Muller, Jann III-543Musolino, Francesco IV-578

Nakamura, Kazuaki II-425Nakata, Michinori I-280Nguyen, Hung Son I-331Nguyen, Sinh Hoa I-331Nicolas, Jean-Louis I-11Nielandt, Joachim I-450Norese, Maria Franca II-68Novello, Chiara II-68Nugent, Chris III-564Nurnberger, Andreas II-35, II-44Nuzillard, Danielle I-11

Okura, Mahito IV-571Onieva, Enrique I-101Osicka, Petr III-221O’Sullivan, Dympna III-142

Author Index 657

Ozkan, Betul IV-423Ozkır, Vildan IV-432

Palau, Manel II-88Pancerz, Krzysztof III-241Pardel, Przemyslaw Wiktor II-550Parillo, Fernando IV-84Pekala, Barbara II-481Pelta, David A. IV-529Pereira, Fernando A. II-107Perfilieva, Irina I-230, II-206Peters, James F. I-586Petrık, Milan III-370Petturiti, Davide III-211Piechowiak, Sylvain III-39Pivert, Olivier I-341, I-351, I-471Pizzi, Nick J. III-132Pons, Jose Enrique I-410, I-420Pons Capote, Olga I-410, I-420Prade, Henri I-21, I-208, I-511, III-306,

III-347, III-420Pradera, Ana III-326Pratelli, Luca IV-114Psaila, Giuseppe I-371Pu, Ida III-181

Quaeghebeur, Erik III-430

Rachunek, Jirı I-596Raghavan, Vijay V. I-501Rakus-Andersson, Elisabeth I-140Ralescu, Anca I-561, IV-509Ramanna, Sheela I-586Ramezani, Fahimeh I-160Rawashdeh, Mohammad I-561Recasens, Jordi III-261Reformat, Marek Z. II-149, III-191Reiser, Renata II-450, III-326Renooij, Silja III-151, III-161Reyneri, Leonardo M. II-78Ricci, Roberto Ghiselli II-181Ricciardello, Angela IV-642Richard, Gilles I-208Rico, Agnes III-306, III-347, IV-268Riera, Juan Vicente II-325Rigo, Pietro IV-114Rinaudo, Salvatore IV-622Rindone, Fabio III-360Rocacher, Daniel I-400Roder, Thomas I-1

Rodrıguez, J. Tinguaro III-317Rodrıguez, Rafael II-395Rodrıguez, Rosa M. I-181Rodrıguez-Muniz, Luis J. IV-499Rojas, Karina III-317Roland, Julien IV-338Rolland, Antoine IV-238Rooney, Niall III-543Roschger, Christoph IV-632Rozenberg, Igor II-98Ruiz-Aguilera, Daniel III-286

Sagara, Nobusumi IV-228Sakai, Hiroshi I-280Salounova, Dana I-596Sampaio Filho, Antonio Carlos IV-10Sanchez, Daniel II-15, II-25, II-285Sanchez, David IV-519Sanchez-Marre, Miquel II-137Sandri, Sandra I-51Sanfilippo, Giuseppe IV-146Schijf, Hermi J.M. III-151Schiliro, Daniele IV-593Schmuck, Roman I-1Schoenke, Jan I-32Scozzafava, Romano IV-168Seising, Rudolf I-541, II-52Sempi, Carlo II-186Serrurier, Mathieu I-21, III-410, III-420Sgarro, Andrea III-1Shariatmadar, Keivan III-430Shenoy, Prakash P. IV-676Shipley, Margaret F. IV-1Shoji, Tetsuo II-425Siebert, Xavier I-613Silva, Ricardo C. III-102Simon, Christophe III-481Skowron, Andrzej I-321Sl ↪ezak, Dominik I-280, II-570S�lowinski, Roman I-300, I-613, I-624,

III-142Smits, Gregory I-341, I-471Sokolov, Oleksandr II-434Sokolowska, Barbara II-550Sorini, Laerte III-69Spata, Massimo Orazio IV-622Spronk, Jaap IV-94Stachowiak, Anna II-441Stading, Gary L. IV-1

658 Author Index

Stamate, Daniel III-181, III-201Stefanini, Luciano III-69, IV-64Stepaniuk, Jaros�law I-321Stroppiana, Daniela I-121Stupnanova, Andrea IV-542Sugeno, Michio I-91, I-111, IV-268Suppan, Lisbeth I-1Synak, Piotr II-570Szel ↪ag, Marcin I-300Szmidt, Eulalia II-529

Tabacchi, Marco Elio II-62Tadeusiewicz, Ryszard III-241Takahagi, Eiichiro IV-218Tanaka, Kazumoto II-405Taniguchi, Tadanari I-111Tanscheit, Ricardo IV-10Tchikapa, Nganteu I-552Teissedre, Charles I-460Termini, Settimo II-62Tettamanzi, Andrea G.B. II-285Thomas, Roland III-142Timonin, Mikhail IV-198Toppin, Graham II-570Tormasi, Alex I-260Torrens, Joan II-315, II-325, III-286Torres-Parejo, Ursula I-390Tran, Minh-Triet I-440Tran, Thai-Son I-440Trillas, Enric II-15, II-25Trivino, Gracian II-295Troiano, Luigi I-61, IV-499Trutschnig, Wolfgang II-161Turunen, Esko I-552Tuzun, Serhat IV-432

Ucal Sarı, Irem IV-441, IV-449Ulukan, H. Ziya IV-413Ustundag, Alp IV-403

Valet, Lionel I-521, I-531Valls, Aıda II-127, IV-519Van Britsom, Daan I-481, II-276Van Camp, Arthur III-460van der Gaag, Linda C. III-151, III-161Vannocci, Marco II-78Vannucci, Marco II-78Vantaggi, Barbara IV-168Vejnarova, Jirina III-450Vellasco, Marley IV-10Vemuri, Nageswara Rao II-365Verdegay, Jose Luis III-102Verly, Celine IV-338Verstraete, Jorg I-361Vesic, Srdjan IV-480Vila, Maria-Amparo I-390Villacorta, Pablo J. I-150, IV-529Villagra, Jorge I-101Vivona, Doretta II-1, III-271, IV-539

Wagenknecht, Michael II-434Waldhauser, Tamas III-347Wallmann, Christian IV-157Wang, Guoyin I-300Wang, Hui II-265, III-543Wang, Xizhao III-112Weissteiner, C.J. I-121Wilk, Szymon III-142Wroblewski, Jakub II-570Wu, Hemin IV-278

Yager, Ronald R. II-149, III-90Yano, Yoneo II-405Yoshitsugu, Kota II-415

Zhang, Guangquan I-170Ziari, Shokrollah III-79Zielinski, Pawe�l IV-74, IV-104Zilles, Karl I-240Zurita, Jose Manuel II-245