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Belief Functions: Theory and Applications

6th International Conference, BELIEF 2021, Shanghai, China, October 15–19, 2021, Proceedings

  • Conference proceedings
  • © 2021

Overview

Editors:
  1. Thierry Denœux

    1. Université de Technologie de Compiègne, Compiègne, France

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  2. Eric Lefèvre

    1. Université d'Artois, Béthune, France

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  3. Zhunga Liu

    1. Northwestern Polytechnical University, Xi'an, China

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  4. Frédéric Pichon

    1. Université d'Artois, Béthune, France

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 12915)

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

Included in the following conference series:

Conference proceedings info: BELIEF 2021.

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Table of contents (30 papers)

  1. Front Matter

    Pages i-xi
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  2. Clustering

    1. Front Matter

      Pages 1-1
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    2. Fast Unfolding of Credal Partitions in Evidential Clustering

      • Zuowei Zhang, Arnaud Martin, Zhunga Liu, Kuang Zhou, Yiru Zhang
      Pages 3-12

    3. Credal Clustering for Imbalanced Data

      • Zuowei Zhang, Zhunga Liu, Kuang Zhou, Arnaud Martin, Yiru Zhang
      Pages 13-21

    4. Evidential Weighted Multi-view Clustering

      • Kuang Zhou, Mei Guo, Ming Jiang
      Pages 22-32

    5. Unequal Singleton Pair Distance for Evidential Preference Clustering

      • Yiru Zhang, Arnaud Martin
      Pages 33-43

  3. Transfer Learning

    1. Front Matter

      Pages 45-45
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    2. Transfer Evidential C-Means Clustering

      • Lianmeng Jiao, Feng Wang, Quan Pan
      Pages 47-55

    3. Evidential Clustering Based on Transfer Learning

      • Kuang Zhou, Mei Guo, Arnaud Martin
      Pages 56-65

    4. Ensemble of Adapters for Transfer Learning Based on Evidence Theory

      • Ying Lv, Bofeng Zhang, Xiaodong Yue, Zhikang Xu, Wei Liu
      Pages 66-75

  4. Classification

    1. Front Matter

      Pages 77-77
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    2. Improving Micro-Extended Belief Rule-Based System Using Activation Factor for Classification Problems

      • Long-Hao Yang, Jun Liu, Ying-Ming Wang, Hui Wang, Luis Martínez
      Pages 79-86

    3. Orbit Classification for Prediction Based on Evidential Reasoning and Belief Rule Base

      • Chao Sun, Xiaoxia Han, Wei He, Hailong Zhu
      Pages 87-95

    4. Imbalance Data Classification Based on Belief Function Theory

      • Jiawei Niu, Zhunga Liu
      Pages 96-104

    5. A Classification Tree Method Based on Belief Entropy for Evidential Data

      • Kangkai Gao, Liyao Ma, Yong Wang
      Pages 105-114

  5. Statistical Inference and Learning

    1. Front Matter

      Pages 115-115
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    2. Entropy-Based Learning of Compositional Models from Data

      • Radim Jiroušek, Václav Kratochvíl, Prakash P. Shenoy
      Pages 117-126

    3. Approximately Valid and Model-Free Possibilistic Inference

      • Leonardo Cella, Ryan Martin
      Pages 127-136

    4. Towards a Theory of Valid Inferential Models with Partial Prior Information

      • Ryan Martin
      Pages 137-146

    5. Ensemble Learning Based on Evidential Reasoning Rule with a New Weight Calculation Method

      • Cong Xu, Zhi-Jie Zhou, Wei He, Hailong Zhu, Yan-Zi Gao
      Pages 147-156
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  1. Belief Functions: Theory and Applications

Keywords

About this book

This book constitutes the refereed proceedings of the 6th International Conference on Belief Functions, BELIEF 2021, held in Shanghai, China, in October 2021. The 30 full papers presented in this book were carefully selected and reviewed from 37 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

  • Université de Technologie de Compiègne, Compiègne, France

    Thierry Denœux

  • Université d'Artois, Béthune, France

    Eric Lefèvre, Frédéric Pichon

  • Northwestern Polytechnical University, Xi'an, China

    Zhunga Liu

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