Belief Functions: Theory and Applications

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

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.

• artificial intelligence
• combination rules
• computer hardware
• computer science
• computer systems
• computer vision
• data fusion
• data mining
• dempster
• dempster-shafer theory
• engineering
• formal logic
• fuzzy sets
• image analysis
• image processing
• machine learning
• mathematics
• sensor data fusion
• sensors
• signal processing

• 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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