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Belief Functions: Theory and Applications

4th International Conference, BELIEF 2016, Prague, Czech Republic, September 21-23, 2016, Proceedings

  • Conference proceedings
  • © 2016

Overview

Editors:
  1. Jiřina Vejnarová

    1. Czech Academy of Sciences, nstitute of Information Theory and Automation, Prague , Czech Republic

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  2. Václav Kratochvíl

    1. Czech Academy of Sciences, Institute of Information Theory and Automation, Prague, Czech Republic

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    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 9861)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: BELIEF 2016.

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Table of contents (25 papers)

  1. Front Matter

    Pages I-XI
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  3. Decission

    1. Front Matter

      Pages 35-35
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    2. Designing an Evidential Assertion Language for Multiple Analysts

      • David Burke
      Pages 37-45

    3. A New ER-MCDA Mapping for Decision-Making Based on Imperfect Information

      • Simon Carladous, Jean-Marc Tacnet, Jean Dezert, Guillaume Dupouy, Mireille Batton-Hubert
      Pages 46-55

    4. Applying ER-MCDA and BF-TOPSIS to Decide on Effectiveness of Torrent Protection

      • Simon Carladous, Jean-Marc Tacnet, Jean Dezert, Deqiang Han, Mireille Batton-Hubert
      Pages 56-65

    5. Decision-Making with Belief Interval Distance

      • Jean Dezert, Deqiang Han, Jean-Marc Tacnet, Simon Carladous, Yi Yang
      Pages 66-74

    6. The BF-TOPSIS Approach for Solving Non-classical MCDM Problems

      • Jean Dezert, Deqiang Han, Jean-Marc Tacnet, Simon Carladous, Hanlin Yin
      Pages 75-83

    7. Use of Evidence Theory in Fault Tree Analysis for Road Safety Inspection

      • Nopadon Kronprasert, Nattika Thipnee
      Pages 84-93

  4. Classification

    1. Front Matter

      Pages 95-95
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    2. Characterization of Experts in Crowdsourcing Platforms

      • Amal Ben Rjab, Mouloud Kharoune, Zoltan Miklos, Arnaud Martin
      Pages 97-104

    3. k-EVCLUS: Clustering Large Dissimilarity Data in the Belief Function Framework

      • Orakanya Kanjanatarakul, Songsak Sriboonchitta, Thierry Denœux
      Pages 105-112

    4. SVM Classifier Fusion Using Belief Functions: Application to Hyperspectral Data Classification

      • Marie Lachaize, Sylvie Le Hégarat-Mascle, Emanuel Aldea, Aude Maitrot, Roger Reynaud
      Pages 113-122

    5. Semi-supervised Evidential Label Propagation Algorithm for Graph Data

      • Kuang Zhou, Arnaud Martin, Quan Pan
      Pages 123-133

  5. Information Fusion

    1. Front Matter

      Pages 135-135
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    2. Conjunctive Rules in the Theory of Belief Functions and Their Justification Through Decisions Models

      • Andrey G. Bronevich, Igor N. Rozenberg
      Pages 137-145

    3. A Relationship of Conflicting Belief Masses to Open World Assumption

      • Milan Daniel
      Pages 146-155
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  1. Belief Functions: Theory and Applications

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About this book

This book constitutes the thoroughly refereed proceedings of the 4th International Conference on Belief Functions, BELIEF 2016, held in Prague, Czech Republic, in September 2016.

The 25 revised full papers presented in this book were carefully selected and reviewed from 33 submissions. The papers describe recent developments of theoretical issues and applications in various areas such as combination rules; conflict management; generalized information theory; image processing; material sciences; navigation.

Editors and Affiliations

  • Czech Academy of Sciences, nstitute of Information Theory and Automation, Prague , Czech Republic

    Jiřina Vejnarová

  • Czech Academy of Sciences, Institute of Information Theory and Automation, Prague, Czech Republic

    Václav Kratochvíl

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