Belief Functions: Theory and Applications: 4th International Conference, BELIEF 2016, Prague, Czech Republic, September 21-23, 2016, Proceedings

255 Pages·2016·16.191 MB·English

by  Jiřina Vejnarová, Václav Kratochvíl (eds.)

Jirina Vejnarová
Václav Kratochvíl (Eds.)
1
6 Belief Functions:
8
9

I
A Theory and Applications
N
L
4th International Conference, BELIEF 2016
Prague, Czech Republic, September 21–23, 2016
Proceedings
123
fi
Lecture Notes in Arti cial Intelligence 9861
Subseries of Lecture Notes in Computer Science
LNAI Series Editors
Randy Goebel
University of Alberta, Edmonton, Canada
Yuzuru Tanaka
Hokkaido University, Sapporo, Japan
Wolfgang Wahlster
DFKI and Saarland University, Saarbrücken, Germany
LNAI Founding Series Editor
Joerg Siekmann
DFKI and Saarland University, Saarbrücken, Germany
More information about this series at http://www.springer.com/series/1244
ř á á í
Ji ina Vejnarov V clav Kratochv l (Eds.)
(cid:129)
Belief Functions:
Theory and Applications
4th International Conference, BELIEF 2016
–
Prague, Czech Republic, September 21 23, 2016
Proceedings
123
Editors
Jiřina Vejnarová Václav Kratochvíl
Institute of Information Theory and Institute of Information Theory and
Automation Automation
Czech Academy ofSciences Czech Academy ofSciences
Prague Prague
Czech Republic Czech Republic
ISSN 0302-9743 ISSN 1611-3349 (electronic)
Lecture Notesin Artificial Intelligence
ISBN 978-3-319-45558-7 ISBN978-3-319-45559-4 (eBook)
DOI 10.1007/978-3-319-45559-4
LibraryofCongressControlNumber:2016949600
LNCSSublibrary:SL7–ArtificialIntelligence
©SpringerInternationalPublishingSwitzerland2016
Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpartofthe
material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation,
broadcasting, reproduction on microfilms or in any other physical way, and transmission or information
storageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilarmethodologynow
knownorhereafterdeveloped.
Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthispublication
doesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevant
protectivelawsandregulationsandthereforefreeforgeneraluse.
Thepublisher,theauthorsandtheeditorsaresafetoassumethattheadviceandinformationinthisbookare
believedtobetrueandaccurateatthedateofpublication.Neitherthepublishernortheauthorsortheeditors
give a warranty, express or implied, with respect to the material contained herein or for any errors or
omissionsthatmayhavebeenmade.
Printedonacid-freepaper
ThisSpringerimprintispublishedbySpringerNature
TheregisteredcompanyisSpringerInternationalPublishingAGSwitzerland
Preface
The theory of belieffunctions, also referred to as evidence theory or Dempster-Shafer
theory, is a well-established general framework for reasoning with uncertainty. It has
well-understood connectionstootherframeworks, suchasprobability,possibility,and
impreciseprobabilitytheories.FirstintroducedbyArthurP.Dempsterinthecontextof
statistical inference, the theory was later developed by Glenn Shafer into a general
framework for modeling epistemic uncertainty. These early contributions have pro-
vided thestartingpointsfor many importantdevelopments,including theTransferable
Belief Model and the Theory of Hints.
The biennial BELIEF conferences (organized by the Belief Functions and Appli-
cations Society) are dedicated to the exchange of ideas, reporting of recent achieve-
ments, and presenting the wide range of applications of this theory. This conference
series was startedinBrest, France, in2010; thesecond event was held inCompiègne,
France, in May 2012; and the third in Oxford, UK, in September 2014.
Thepresentvolumecontainstheproceedingsofthe4thInternationalConferenceon
Belief Functions, which took place in Prague, Czech Republic, on September 21–23,
2016. The book contains 25 peer-reviewed papers (out of a total number of 33 sub-
missions)describingrecentdevelopmentsconcerningboththeoreticalissues(including
combination rules, conflict management, and generalized information theory) and
applications in various areas (such as image processing, material sciences, and
navigation).
The editors wouldlike tothank all those whocontributed tothisvolumeaswell as
those who helped with organizing the conference. We would especially like to thank
Thierry Denœux and Arnaud Martin, members of the Steering Committee, whose
experience with conference organization was invaluable, and the members of the
ProgramCommitteeandexternalreviewersforcarefullyreviewingallthesubmissions.
We would also like to thank the administration of the Institute of Information Theory
andAutomation oftheCzechAcademy ofSciences,wheretheconferencetook place.
July 2016 Jiřina Vejnarová
Václav Kratochvíl
Organization
Steering Committee
Fabio Cuzzolin Oxford Brookes University, UK
Thierry Denœux Université de Technologie de Compiègne, France
Arnaud Martin Université de Rennes1/IRISA, France
Program Committee
Alessandro Antonucci IDSIA, Switzerland
Giulianella Coletti Università degli Studi di Perugia, Italy
Olivier Colot Université Lille 1, France
Frank Coolen Durham University, UK
Inés Couso University of Oviedo, Spain
Fabio Cuzzolin Oxford Brookes University, UK
Milan Daniel Jan Becher - Karlovarská Becherovka, a.s.,
Czech Republic
Thierry Denœux Université de Technologie de Compiègne, France
Sébastien Destercke CNRS, UMR Heudiasyc, France
Jean Dezert Onera, France
Zied Elouedi Institut Supérieur de Gestion de Tunis, Tunisia
Thomas Fetz University of Innsbruck, Austria
Michel Grabisch Université Paris 1, France
Radim Jiroušek University of Economics, Czech Republic
Anne-Laure Jousselme NATO CMRE, Italy
Tomáš Kroupa Czech Academy of Sciences, Czech Republic
Éric Lefèvre LGI2A Université d’Artois, France
Weiru Liu Queen’s University Belfast, UK
Liping Liu The University of Akron, USA
Arnaud Martin Université de Rennes1/IRISA, France
David Mercier Université d’Artois, France
Enrique Miranda University of Oviedo, Spain
Serafin Moral University of Granada, Spain
Michael Oberguggenberger University of Innsbruck, Austria
Michele Rombaut Gipsa-lab, France
Prakash P. Shenoy University of Kansas School of Business, USA
Johan Schubert Swedish Defence Research Agency, Sweden
Barbara Vantaggi Università “La Sapienza”, Italy
Jiřina Vejnarová Czech Academy of Sciences, Czech Republic
VIII Organization
Paolo Vicig University of Trieste, Italy
Ronald Yager Machine Intelligence Institute - Iona College, USA
BELIEF 2016 was organized by the Institute of Information Theory and Automation,
Czech Academy of Sciences on behalf of BFAS - Belief Functions and Applications
Society.
Local Organizing Committee
Jiřina Vejnarová (Chair) Czech Academy of Sciences, Czech Republic
Milan Daniel Jan Becher - Karlovarská Becherovka, a.s.,
Czech Republic
Radim Jiroušek University of Economics, Czech Republic
Václav Kratochvíl Czech Academy of Sciences, Czech Republic
Support
The conference was supported by:
Belief Functions and Applications Society
Institute of Information Theory and Automation, Czech Academy of Sciences
Contents
Theoretical Issues
Entropy of Belief Functions in the Dempster-Shafer Theory:
A New Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Radim Jiroušek and Prakash P. Shenoy
A New Matrix Addition Rule for Combining Linear Belief Functions . . . . . . 14
Liping Liu
On Internal Conflict as an External Conflict of a Decomposition
of Evidence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Alexander Lepskiy
Decission
Designing an Evidential Assertion Language for Multiple Analysts. . . . . . . . 37
David Burke
A New ER-MCDA Mapping for Decision-Making Based on Imperfect
Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
Simon Carladous, Jean-Marc Tacnet, Jean Dezert, Guillaume Dupouy,
and Mireille Batton-Hubert
Applying ER-MCDA and BF-TOPSIS to Decide on Effectiveness
of Torrent Protection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
Simon Carladous, Jean-Marc Tacnet, Jean Dezert, Deqiang Han,
and Mireille Batton-Hubert
Decision-Making with Belief Interval Distance. . . . . . . . . . . . . . . . . . . . . . 66
Jean Dezert, Deqiang Han, Jean-Marc Tacnet, Simon Carladous,
and Yi Yang
The BF-TOPSIS Approach for Solving Non-classical MCDM Problems . . . . 75
Jean Dezert, Deqiang Han, Jean-Marc Tacnet, Simon Carladous,
and Hanlin Yin
Use of Evidence Theory in Fault Tree Analysis for Road Safety Inspection . . . 84
Nopadon Kronprasert and Nattika Thipnee
Classification
Characterization of Experts in Crowdsourcing Platforms . . . . . . . . . . . . . . . 97
AmalBenRjab,MouloudKharoune,ZoltanMiklos,andArnaudMartin
X Contents
k-EVCLUS: Clustering Large Dissimilarity Data in the Belief
Function Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
OrakanyaKanjanatarakul,SongsakSriboonchitta,andThierryDenœux
SVM Classifier Fusion Using Belief Functions: Application to
Hyperspectral Data Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
Marie Lachaize, Sylvie Le Hégarat-Mascle, Emanuel Aldea,
Aude Maitrot, and Roger Reynaud
Semi-supervised Evidential Label Propagation Algorithm for Graph Data. . . . 123
Kuang Zhou, Arnaud Martin, and Quan Pan
Information Fusion
ConjunctiveRulesintheTheoryofBeliefFunctionsandTheirJustification
Through Decisions Models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
Andrey G. Bronevich and Igor N. Rozenberg
A Relationship of Conflicting Belief Masses to Open World Assumption. . . . 146
Milan Daniel
Idempotent Conjunctive Combination of Belief Functions
by Distance Minimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156
John Klein, Sebastien Destercke, and Olivier Colot
IPFP and Further Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164
Václav Kratochvíl and Jiřina Vejnarová
Entropy-Based Counter-Deception in Information Fusion. . . . . . . . . . . . . . . 174
Johan Schubert
Identification of Elastic Properties Based on Belief Function Inference . . . . . 182
Liqi Sui, Pierre Feissel, and Thierry Denœux
D-S Theory for Argument Confidence Assessment . . . . . . . . . . . . . . . . . . . 190
Rui Wang, Jérémie Guiochet, Gilles Motet, and Walter Schön
Applications
Evidential Correlated Gaussian Mixture Markov Model for Pixel
Labeling Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203
LinAn,MingLi,MohamedElYazidBoudaren,andWojciechPieczynski
The Capacitated Vehicle Routing Problem with Evidential Demands:
A Belief-Constrained Programming Approach. . . . . . . . . . . . . . . . . . . . . . . 212
Nathalie Helal, Frédéric Pichon, Daniel Porumbel, David Mercier,
and Éric Lefèvre