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Belief Functions: Theory and Applications

7th International Conference, BELIEF 2022, Paris, France, October 26–28, 2022, Proceedings

  • Conference proceedings
  • © 2022

Overview

Editors:
  1. Sylvie Le Hégarat-Mascle

    1. University of Paris-Saclay, Gif sur Yvette, France

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  2. Isabelle Bloch

    1. Sorbonne University, Paris, France

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    You can also search for this editor in PubMed   Google Scholar

  3. Emanuel Aldea

    1. University of Paris-Saclay, Gif sur Yvette, France

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 13506)

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

Included in the following conference series:

Conference proceedings info: BELIEF 2022.

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Table of contents (29 papers)

  1. Front Matter

    Pages i-xi
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  2. Evidential Clustering

    1. Front Matter

      Pages 1-1
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    2. A Distributional Approach for Soft Clustering Comparison and Evaluation

      • Andrea Campagner, Davide Ciucci, Thierry Denœux
      Pages 3-12

    3. Causal Transfer Evidential Clustering

      • Kuang Zhou, Ming Jiang
      Pages 13-22

    4. A Variational Bayesian Clustering Approach to Acoustic Emission Interpretation Including Soft Labels

      • Martin Mbarga Nkogo, Emmanuel Ramasso, Patrice Le Moal, Gilles Bourbon
      Pages 23-32

    5. Evidential Clustering by Competitive Agglomeration

      • Lulu Xu, Qian Wang, Pei-hong Wang, Zhi-gang Su
      Pages 33-43

    6. Imperfect Labels with Belief Functions for Active Learning

      • Arthur Hoarau, Arnaud Martin, Jean-Christophe Dubois, Yolande Le Gall
      Pages 44-53

  3. Machine Learning and Pattern Recognition

    1. Front Matter

      Pages 55-55
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    2. An Evidential Neural Network Model for Regression Based on Random Fuzzy Numbers

      • Thierry Denœux
      Pages 57-66

    3. Ordinal Classification Using Single-Model Evidential Extreme Learning Machine

      • Liyao Ma, Peng Wei, Bin Sun
      Pages 67-76

    4. Reliability-Based Imbalanced Data Classification with Dempster-Shafer Theory

      • Hongpeng Tian, Zuowei Zhang, Arnaud Martin, Zhunga Liu
      Pages 77-86

    5. Evidential Regression by Synthesizing Feature Selection and Parameters Learning

      • Chao Liu, Zhi-gang Su, Gang Zhao
      Pages 87-95

  4. Algorithms and Evidential Operators

    1. Front Matter

      Pages 97-97
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    2. Distributed EK-NN Classification

      • Chaoyu Gong, Zhi-gang Su, Qian Wang, Yang You
      Pages 99-108

    3. On Improving a Group of Evidential Sources with Different Contextual Corrections

      • Siti Mutmainah, Samir Hachour, Frédéric Pichon, David Mercier
      Pages 109-118

    4. Measure of Information Content of Basic Belief Assignments

      • Jean Dezert, Albena Tchamova, Deqiang Han
      Pages 119-128

    5. Belief Functions on Ordered Frames of Discernment

      • Arnaud Martin
      Pages 129-138

    6. On Modelling and Solving the Shortest Path Problem with Evidential Weights

      • Tuan-Anh Vu, Sohaib Afifi, Éric Lefèvre, Frédéric Pichon
      Pages 139-149

  5. Data and Information Fusion

    1. Front Matter

      Pages 151-151
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    2. Heterogeneous Image Fusion for Target Recognition Based on Evidence Reasoning

      • Shuyue Wang, Zhunga Liu, Zuowei Zhang, Yang Li
      Pages 153-162
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  1. Belief Functions: Theory and Applications

Keywords

About this book

This book constitutes the refereed proceedings of the 7th International Conference on Belief Functions, BELIEF 2022, held in Paris, France, in October 2022.

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. It has been applied in diverse areas such as machine learning, information fusion, and pattern recognition.

The 29 full papers presented in this book were carefully selected and reviewed from 31 submissions. The papers cover a wide range on theoretical aspects on mathematical foundations, statistical inference as well as on applications in various areas including classification, clustering, data fusion, image processing, and much more.

Editors and Affiliations

  • University of Paris-Saclay, Gif sur Yvette, France

    Sylvie Le Hégarat-Mascle, Emanuel Aldea

  • Sorbonne University, Paris, France

    Isabelle Bloch

Bibliographic Information

  • Book Title: Belief Functions: Theory and Applications

  • Book Subtitle: 7th International Conference, BELIEF 2022, Paris, France, October 26–28, 2022, Proceedings

  • Editors: Sylvie Le Hégarat-Mascle, Isabelle Bloch, Emanuel Aldea

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-031-17801-6

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022

  • Softcover ISBN: 978-3-031-17800-9Published: 01 October 2022

  • eBook ISBN: 978-3-031-17801-6Published: 29 September 2022

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XI, 317

  • Number of Illustrations: 13 b/w illustrations, 40 illustrations in colour

  • Topics: Probability Theory and Stochastic Processes

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