Belief Functions: Theory and Applications

1. Front Matter

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2. Belief Combination

   1. α-Junctions of Categorical Mass Functions

      • John Klein, Mehena Loudahi, Jean-Marc Vannobel, Olivier Colot

      Pages 1-10

   2. Truthfulness in Contextual Information Correction

      • Frédéric Pichon, David Mercier, François Delmotte, Éric Lefèvre

      Pages 11-20

   3. The Choice of Generalized Dempster-Shafer Rules for Aggregating Belief Functions Based on Imprecision Indices

      • Andrey G. Bronevich, Igor N. Rozenberg

      Pages 21-28

   4. General Schemes of Combining Rules and the Quality Characteristics of Combining

      • Alexander Lepskiy

      Pages 29-38

   5. An Optimal Unified Combination Rule

      • Yanyan He, M. Yousuff Hussaini

      Pages 39-48

3. Machine Learning

   1. Evidential Logistic Regression for Binary SVM Classifier Calibration

      • Philippe Xu, Franck Davoine, Thierry Denoeux

      Pages 49-57

   2. The Evidence-Theoretic k-NN Rule for Rank-Ordered Data: Application to Predict an Individual’s Source of Loan

      • Supanika Leurcharusmee, Peerapat Jatukannyaprateep, Songsak Sriboonchitta, Thierry Denoeux

      Pages 58-67

   3. Belief Hierarchical Clustering

      • Wiem Maalel, Kuang Zhou, Arnaud Martin, Zied Elouedi

      Pages 68-76

   4. Logistic Regression of Soft Labeled Instances via the Evidential EM Algorithm

      • Benjamin Quost

      Pages 77-86

   5. Training and Evaluating Classifiers from Evidential Data: Application to E²M Decision Tree Pruning

      • Nicolas Sutton-Charani, Sébastien Destercke, Thierry Denœux

      Pages 87-94

   6. Reflections on DS/AHP: Lessons to Be Learnt

      • Malcolm J. Beynon

      Pages 95-104

4. Applications 1

   1. Evidential Database: A New Generalization of Databases?

      • Ahmed Samet, Éric Lefèvre, Sadok Ben Yahia

      Pages 105-114

   2. Belief Approach for Social Networks

      • Salma Ben Dhaou, Mouloud Kharoune, Arnaud Martin, Boutheina Ben Yaghlane

      Pages 115-123

   3. Methods Handling Accident and Traffic Jam Information with Belief Functions in VANETs

      • Mira Bou Farah, David Mercier, François Delmotte, Éric Lefèvre, Sylvain Lagrue

      Pages 124-133

   4. Designing a Belief Function-Based Accessibility Indicator to Improve Web Browsing for Disabled People

      • Jean-Christophe Dubois, Yolande Le Gall, Arnaud Martin

      Pages 134-142

   5. Belief Reasoning Model for Mapping Public Participation in Transport Planning

      • Nopadon Kronprasert, Antti P. Talvitie

      Pages 143-152

5. Theory 1

   1. Some Notes on Canonical Decomposition and Separability of a Belief Function

      • Xiaolu Ke, Liyao Ma, Yong Wang

      Pages 153-160

   2. A Relational Representation of Belief Functions

      • Liping Liu

      Pages 161-170

   3. Modeling Qualitative Assessments under the Belief Function Framework

      • Amel Ennaceur, Zied Elouedi, Éric Lefevre

      Pages 171-179

This book constitutes the thoroughly refereed proceedings of the Third International Conference on Belief Functions, BELIEF 2014, held in Oxford, UK, in September 2014. The 47 revised full papers presented in this book were carefully selected and reviewed from 56 submissions. The papers are organized in topical sections on belief combination; machine learning; applications; theory; networks; information fusion; data association; and geometry.

• Vehicular Ad-hoc Network (VANET)
• bayesian inference
• belief reasoning
• classification
• decision making
• document analysis
• evidential databases
• human factors
• object recognition
• on-destructive testing
• partially-supervised learning
• probabilistic database
• social networks
• statistical inference
• support vector machines
• target classification
• target tracking

• Department of Computing and Communications Technologies, Oxford Brookes University, Oxford, UK

  Fabio Cuzzolin

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