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Belief Functions: Theory and Applications

Proceedings of the 2nd International Conference on Belief Functions, Compiègne, France 9-11 May 2012

  • Conference proceedings
  • © 2012

Overview

Editors:
  1. Thierry Denoeux

    1. , Centre de Recherches de Royallieu, Université de Technologie de Compiègne, Compiègne, France

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  2. Marie-Hélène Masson

    1. Université de Picardie Jules Verne, Compiègne, France

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  • Latest research on theory and applications Belief functions
  • Results of the 2nd International Conference on Belief Functions, Compiègne, France 9-11 May 2012
  • Written by leading experts in the field

Part of the book series: Advances in Intelligent and Soft Computing (AINSC, volume 164)

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About this book

The theory of belief functions, also known as evidence theory or Dempster-Shafer theory, was first introduced by Arthur P. Dempster in the context of statistical inference, and was later developed by Glenn Shafer as a general framework for modeling epistemic uncertainty. These early contributions have been the starting points of many important developments, including the Transferable Belief Model and the Theory of Hints. The theory of belief functions is now well established as a general framework for reasoning with uncertainty, and has well understood connections to other frameworks such as probability, possibility and imprecise probability theories.

 

This volume contains the proceedings of the 2nd International Conference on Belief Functions that was held in Compiègne, France on 9-11 May 2012. It gathers 51 contributions describing recent developments both on theoretical issues (including approximation methods, combination rules, continuous belief functions, graphical models and independence concepts) and applications in various areas including classification, image processing, statistics and intelligent vehicles.

 

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Table of contents (51 papers)

  1. Front Matter

    Pages 1-9
    Download chapter PDF

  2. On Belief Functions and Random Sets

    • Hung T. Nguyen
    Pages 1-19

  3. Evidential Multi-label Classification Using the Random k-Label Sets Approach

    • Sawsan Kanj, Fahed Abdallah, Thierry Denœux
    Pages 21-28

  4. An Evidential Improvement for Gender Profiling

    • Jianbing Ma, Weiru Liu, Paul Miller
    Pages 29-36

  5. An Interval-Valued Dissimilarity Measure for Belief Functions Based on Credal Semantics

    • Alessandro Antonucci
    Pages 37-44

  6. An Evidential Pattern Matching Approach for Vehicle Identification

    • Anne-Laure Jousselme, Patrick Maupin
    Pages 45-52

  7. A Comparison between a Bayesian Approach and a Method Based on Continuous Belief Functions for Pattern Recognition

    • Anthony Fiche, Arnaud Martin, Jean-Christophe Cexus, Ali Khenchaf
    Pages 53-60

  8. Prognostic by Classification of Predictions Combining Similarity-Based Estimation and Belief Functions

    • Emmanuel Ramasso, Michèle Rombaut, Noureddine Zerhouni
    Pages 61-68

  9. Adaptive Initialization of a EvKNN Classification Algorithm

    • Stefen Chan Wai Tim, Michèle Rombaut, Denis Pellerin
    Pages 69-76

  10. Classification Trees Based on Belief Functions

    • Nicolas Sutton-Charani, Sébastien Destercke, Thierry Denœux
    Pages 77-84

  11. Combination of Supervised and Unsupervised Classification Using the Theory of Belief Functions

    • Fatma Karem, Mounir Dhibi, Arnaud Martin
    Pages 85-92

  12. Continuous Belief Functions: Focal Intervals Properties

    • Jean-Marc Vannobel
    Pages 93-100

  13. Game-Theoretical Semantics of Epistemic Probability Transformations

    • Fabio Cuzzolin
    Pages 101-108

  14. Generalizations of the Relative Belief Transform

    • Fabio Cuzzolin
    Pages 109-116

  15. Choquet Integral as Maximum of Integrals with Respect to Belief Functions

    • Mikhail Timonin
    Pages 117-124

  16. Consonant Approximations in the Belief Space

    • Fabio Cuzzolin
    Pages 125-133

  17. Controling the Number of Focal Elements

    • Christophe Osswald
    Pages 135-143

  18. Random Generation of Mass Functions: A Short Howto

    • Thomas Burger, Sébastien Destercke
    Pages 145-152

  19. Revisiting the Notion of Conflicting Belief Functions

    • Sébastien Destercke, Thomas Burger
    Pages 153-160

  20. About Conflict in the Theory of Belief Functions

    • Arnaud Martin
    Pages 161-168
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Editors and Affiliations

  • , Centre de Recherches de Royallieu, Université de Technologie de Compiègne, Compiègne, France

    Thierry Denoeux

  • Université de Picardie Jules Verne, Compiègne, France

    Marie-Hélène Masson

Bibliographic Information

  • Book Title: Belief Functions: Theory and Applications

  • Book Subtitle: Proceedings of the 2nd International Conference on Belief Functions, Compiègne, France 9-11 May 2012

  • Editors: Thierry Denoeux, Marie-Hélène Masson

  • Series Title: Advances in Intelligent and Soft Computing

  • DOI: https://doi.org/10.1007/978-3-642-29461-7

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2012

  • Softcover ISBN: 978-3-642-29460-0Published: 27 April 2012

  • eBook ISBN: 978-3-642-29461-7Published: 26 April 2012

  • Series ISSN: 1867-5662

  • Series E-ISSN: 1867-5670

  • Edition Number: 1

  • Number of Pages: XII, 444

  • Number of Illustrations: 42 b/w illustrations, 54 illustrations in colour

  • Topics: Computational Intelligence, Artificial Intelligence

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