6th International Conference on Belief Functions, October 15-17, 2021, Shanghai, China.

Collocated with The 1st International Conference on Cognitive Analytics, Granular Computing, and Three-Way Decisions (CCGT 2021)

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Next conference: the Seventh International Conference on Belief Functions (BELIEF 2022) will be held in Paris, France, on October 26-28, 2022.

The international conference dedicated to belief functions

The theory of belief functions, also referred to 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 modelling 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. It has been applied in diverse areas such as machine learning, information fusion and risk analysis.

The BELIEF conferences, sponsored by the Belief Functions and Applications Society, are dedicated to the confrontation of ideas, the reporting of recent achievements and the presentation of the wide range of applications of this theory. The first edition of this conference series was held in Brest, France, in 2010. Later editions were held in Compiègne, France in 2012, Oxford, UK in 2014, Prague, Czech Republic in 2016, and again in Compiègne, France in 2018. The Sixth International Conference on Belief Functions (BELIEF 2021) will be located in Shanghai, China, on October 15-17, 2021, together with the 2021 International Conference on Cognitive analytics, Granular computing, and Three-way decisions (CCGT). It will be held both onsite and online due to the COVID-19 situation (see Venue and Registration below for details).

Important dates

  • May 9: final submission deadline
  • June 25: Author notification
  • July 15: Camera-ready copy due
  • July 16: Application deadline for BFAS Grants for students (closed)
  • October 15-17: Conference


Proceedings of BELIEF 2021 will be published by Springer-Verlag in a volume of the Lecture Notes in Artificial Intelligence (LNCS/LNAI) series and indexed by: ISI Web of Science; EI Engineering Index; ACM Digital Library; dblp; Google Scholar; IO-Port; MathSciNet; Scopus; Zentralblatt MATH. Previous BELIEF proceedings can be found on SpringerLink.

IJAR Best Paper Award

Thanks to the continued support of the International Journal of Approximate Reasoning, the best papers presented at the conference will be distinguished by the IJAR Best Paper Award. The prize will consist of a certificate and 1000 euros, which will be split between the winners.

Author instructions

Authors should submit their papers through Easychair - conference BELIEF 2021 following the Springer LNCS/LNAI series template, also available in Overleaf.

The expected length of papers is no longer than 10 pages, references included, that should present original contributions with significant results. Springer encourages authors to include their ORCIDs in their papers. In addition, the corresponding author of each accepted paper, acting on behalf of all of the authors of that paper, will have to complete and sign a Consent-to-Publish form. The corresponding author signing the copyright form should match the corresponding author marked on the paper. Once the files have been sent to Springer, changes relating to the authorship of the papers cannot be made.


Original contributions are solicited on theoretical aspects including, but not limited to

  • Combination rules
  • Conditioning
  • Continuous belief functions
  • Independence and graphical models
  • Geometry and distance metrics
  • Mathematical foundations
  • Computational frameworks
  • Data and information fusion
  • Links with other uncertainty theories
  • Tracking and data association
  • Statistical Inference
  • Machine Learning and Pattern recognition
  • Evidential Clustering and Classification

as well as on applications to various areas including, but not limited to

  • Applications in network analysis
  • Applications in environment and climate change
  • Applications in biology and medical diagnosis
  • Applications in risk and reliability analysis
  • Applications in business and economics
  • Applications in vision and image processing

IJAR Special issue

Authors of selected papers from the BELIEF 2021 conference will be invited to submit extended versions of their papers for possible inclusion in a special issue of the International Journal of Approximate Reasoning.


The program of this edition of the BELIEF conference will include tutorials from experts on belief functions and their applications.

Confirmed tutorials:

  • Generalized Dempster-Shafer theory based on random fuzzy sets (Prof. Thierry Denœux, Université de technologie de Compiègne, France)
  • Introduction to information fusion in belief function theory (Prof. Frédéric Pichon, Université d’Artois, Béthune, France).

Venue and Registration

To accommodate for the uncertainties surrounding travel possibilities due to the COVID-19 pandemic, participants have two options to attend the conference: either online or onsite.

The onsite event will take place at the Gu Cun Park Hotel (No.4788 Hu Tai Road, Baoshan District, Shanghai). The on-site participants can arrive to hotel by any ways (such as taxi, bus or Line 7 subway).

To facilitate offline communication, a Wechat group for on-site participants have also been created.

Online participants will be able to join and participate live to the onsite event via this link (R1 platform). We expect most keynote talks to be given onsite (and tutorials to be given online). The conference will also be recorded and the recording made available on the R1 platform of the conference:

The registration fee for the onsite participants includes the following items:

Online participants benefit from a discounted registration fee, which includes the following items:

Registration feesRMBEuro
on-site student1000130
on-site faculty1000130
online student384.650
online faculty769.2100
Additional ticket for gala dinner15020
Additional ticket for welcome reception13017

Registration (update - October 14, 2021)

On-site and online participants have to register using link (R1 platform) and have to pay the registration fees described on this page (100€ for a non-student participant, 50€ for a student participant). Each on-site participant will have to pay the registration fee 130€ for all the on-site conference services.

Program (pdf)

(Shanghai time UTC+8)

  BELIEF proceedings can be found here SpringerLink - Free access granted until end of November 2021.

Friday October 15
13:00 13:50   On-site registration
13:50 14:00   Conference opening
14:00 15:00 Session 1 (Classification)
15:00 15:30 Session 2 (Information Fusion)
16:00 17:00 Session 3 (Statistical Inference and Learning)
17:00 17:30   Q&A Tutorials


Saturday October 16
10:00 11:00   Keynote by Van Nam Huynh
  Machine Learning coupled with Evidential Reasoning for User Preference
Chair: Xiaodong Yue
11:00 12:00   Keynote by Chunlai Zhou
  Basic Utility Theory for Belief Functions
Chair: Zhunga Liu
14:00 14:30 Session 4 (Elicitation)
14:30 15:30 Session 5 (Deep Learning)
16:00 17:00 Session 6 (Conflict, inconsistency and specificity)
17:00 18:00   BFAS General Assembly


Sunday October 17
10:00 11:00   Keynote by Deqiang Han
  Learning-based Modelized Methods for Evidence Combination
Chair: Zhunga Liu
11:00 12:00   Keynote by Zengjing Chen
  A Central Limit Theorem for Sets of Probability Measures
Chair: Xiaodong Yue
14:00 15:00 Session 7 (Clustering)
15:30 16:15 Session 8 (Transfer Learning)
16:15 17:00 Session 9 (Algorithms and Computation)
17:00 17:10   Conference closure


Instruction for oral presentation

Each presentation is scheduled to be 15 minutes long, including questions. Instruction to chairman is to leave 10 minutes for the presentation itself and 5 minutes for questions/discussions



Session 1 (Classification) (chair: Liyao Ma)
Improving Micro-Extended Belief Rule-Based System using Activation Factor for Classification Problems
Long-Hao Yang, Jun Liu, Ying-Ming Wang, Hui Wang and Luis Martínez
Orbit Classification for Prediction Based on Evidential Reasoning and Belief Rule Base
Chao Sun, Xiaoxia Han, Wei He and Hailong Zhu
Imbalance Data Classification Based on Belief Function Theory
Jiawei Niu and Zhunga Liu
A Classification Tree Method Based on Belief Entropy for Evidential Data
Kangkai Gao, Liyao Ma and Yong Wang


Session 2 (Information Fusion) (chair: Frédéric Pichon)
A New Multi-Source Information Fusion Method Based on Belief Divergence Measure and the Negation of Basic Probability Assignment
Hongfei Wang, Wen Jiang, Xinyang Deng and Jie Geng
Improving an Evidential Source of Information Using Contextual Corrections Depending on Partial Decisions
Siti Mutmainah, Samir Hachour, Frédéric Pichon and David Mercier


Session 3 (Statistical Inference and Learning) (chair: Ryan Martin)
Entropy-based Learning of Compositional Models from Data
Radim Jiroušek, Václav Kratochvíl and Prakash P. Shenoy
Approximately Valid and Model-Free Possibilistic Inference
Leonardo Cella and Ryan Martin
Towards a Theory of Valid Inferential Models with Partial Prior Information
Ryan Martin
Ensemble Learning Based on Evidential Reasoning Rule with a New Weight Calculation Method
Cong Xu, Zhi-Jie Zhou, Wei He, Hailong Zhu and Yan-Zi Gao


Session 4 (Elicitation) (chair: Arnaud Martin)
Validation of Smets’ Hypothesis in the Crowdsourcing Environment
Constance Thierry, Arnaud Martin, Jean-Christophe Dubois and Yolande Le Gall
Quantifying Confidence of Safety Cases with Belief Functions
Yassir Idmessaoud, Didier Dubois and Jérémie Guiochet


Session 5 (Deep Learning) (chair: Thierry Denœux)
Evidential Segmentation of 3D PET/CT Images
Ling Huang, Su Ruan, Pierre Decazes and Thierry Denœux
Fusion of Evidential CNN Classifiers for Image Classification
Zheng Tong, Philippe Xu and Thierry Denœux
Multi-branch Recurrent Attention Convolutional Neural Network with Evidence Theory for Fine-grained Image Classification
Zhikang Xu, Bofeng Zhang, Haijie Fu, Xiaodong Yue and Ying Lv
Deep Evidential Fusion Network for Image Classification
Shaoxun Xu, Yufei Chen, Chao Ma and Xiaodong Yue


Session 6 (Conflict, inconsistency and specificity) (chair: Anne-Laure Jousselme)
Conflict Measure of Belief Functions with Blurred Focal Elements on the Real Line
Alexander Lepskiy
Logical and Evidential Inconsistencies Meet: First Steps
Nadia Ben Abdallah, Sébastien Destercke, Anne-Laure Jousselme and Frédéric Pichon
A Note About Entropy and Inconsistency in Evidence Theory
Anne-Laure Jousselme, Frédéric Pichon, Nadia Ben Abdallah and Sébastien Destercke
An Extension of Specificity-Based Approximations to Other Belief Function Relations
Tekwa Tedjini, Sohaib Afifi, Frédéric Pichon and Éric Lefèvre


Session 7 (Clustering) (chair: Kuang Zhou)
Fast Unfolding of Credal Partitions in Evidential Clustering
Zuowei Zhang, Arnaud Martin, Zhunga Liu, Kuang Zhou and Yiru Zhang
Credal Clustering for Imbalanced Data
Zuowei Zhang, Zhunga Liu, Kuang Zhou, Arnaud Martin and Yiru Zhang
Evidential Weighted Multi-View Clustering
Kuang Zhou, Mei Guo and Ming Jiang
Unequal Singleton Pair Distance for Evidential Preference Clustering
Yiru Zhang and Arnaud Martin


Session 8 (Transfer Learning) (chair: Lianmeng Jiao)
Transfer Evidential C-means Clustering
Lianmeng Jiao, Feng Wang and Quan Pan
Evidential Clustering Based on Transfer Learning
Kuang Zhou, Mei Guo and Arnaud Martin
Ensemble of Adapters for Transfer Learning Based on Evidence Theory
Ying Lv, Bofeng Zhang, Xiaodong Yue, Zhikang Xu and Wei Liu


Session 9 (Algorithms and Computation) (chair: Juan Jesús Salamanca)
Discussions on the Connectedness of a Random Closed Set
Juan Jesús Salamanca
An Efficient Computation of Dempster-Shafer Theory of Evidence Based on Native GPU Implementation
Noelia Rico, Luigi Troiano and Irene Díaz
QLEN: Quantum-Like Evidential Networks for Predicting the Decision in Prisoner’s Dilemma
Jixiang Deng and Yong Deng


Keynote speakers

Keynote speakers

Van Nam Huynh's photo

Professor Van Nam Huynh, Japan Advanced Institute of Science and Technology, Japan.

Title: Machine Learning coupled with Evidential Reasoning for User Preference

Abstract: Inferring user preferences from short texts generated by users on social platforms has a variety of applications in web-based decision support systems such as recommender systems and personalized marketing systems. Developing an efficient solution to this problem is still challenging due to difficulty in handling short texts and dynamic change of user preferences over time. In this talk, we will present a novel framework that tackles these challenges by combining advanced Machine Learning techniques for concept learning and Dempster-Shafer theory (DST) for reasoning and fusion to effectively infer user preferences. Two instances of the proposed framework will be demonstrated with experimental results and analysis that show the effectiveness and practicality of the developed methods.

Chunlai Zhou's photo

Ass. Professor Chunlai Zhou, Renmin University, China.

Title: Basic Utility Theory for Belief Functions

Abstract: I will talk about a basic utility theory for belief functions which is common ground for different decision theories in Dempster-Shafer theory where the completeness requirement is dropped. The resulting preference relation is represented by subjective expectation of sets of utilities whose ordering is based on an ordering of outcome sets derived from a logical decision theory for complete ignorance. Moreover, we explore the preference aggregation problem within the utility theory and generalize some results by Harsanyi and Mongin to the setting of belief functions.

Deqiang Han's photo

Professor Deqiang Han, Xi'an Jiaotong University, China.

Title: Learning-based Modelized Methods for Evidence Combination

Abstract: Evidence combination is typical uncertainty reasoning or information fusion in the theory of belief functions, which combines bodies of evidence stemming from different information sources. In traditional applications of evidence combination (e.g., pattern classification), given a sample, the basic belief assignments (BBAs) of different information sources are generated first, and then they are combined by a rule, e.g., Dempster's rule. We propose a modelized method for evidence combination. By just inputting the sample into the learned model of combination, a “combined” BBA is obtained. That is, it does not need to generate multiple BBAs for each sample for the combination. In our proposed modelized combination, one can generate different combination models with different combination rules. Experimental results and related analyses validate the related rationality and efficiency.

ZengjingChen's photo

Professor Zengjing Chen, Shandong University, China.

Title: A Central Limit Theorem for Sets of Probability Measures

Abstract: We prove a central limit theorem for a sequence of random variables whose means are ambiguous and vary in an unstructured way. Their joint distribution is described by a set of measures. The limit is (not the normal distribution and is) defined by a backward stochastic differential equation that can be interpreted as modeling an ambiguous continuous-time random walk.


BELIEF-CCGT Conference co-chairs

  • Thierry Denœux, Université de Technologie de Compiègne, France
  • Duoqian Miao, Tongji University, Shanghai, China
  • Yiyu Yao, University of Regina, Canada

BELIEF 2021 Program Committee co-chairs

  • Zhunga Liu, Northwestern Polytechnical University, Xian, China
  • Frédéric Pichon, Université d'Artois, France

BELIEF 2021 Steering committee

  • Éric Lefèvre, Université d'Artois, France
  • Zhunga Liu, Northwestern Polytechnical University, Xian, China
  • David Mercier, Université d'Artois, France
  • Frédéric Pichon, Université d'Artois, France
  • Zhihua Wei, Tongji University, Shanghai, China
  • Xiaodong Yue, Shanghai University, China

BELIEF 2021 Publication chair

  • Éric Lefèvre, Université d'Artois, France

BELIEF 2021 Publicity co-chairs

  • David Mercier, Université d'Artois, France
  • Xiaodong Yue, Shanghai University, China

BELIEF 2021 Organisation committee

  • Xiaodong Yue, Shanghai University, China

BELIEF 2021 Program committee

  • Alessandro Antonucci, Dalle Molle Institute for Artificial Intelligence, Lugano, Switzerland
  • Olivier Colot, Université de Lille, France
  • Ines Couso, University of Oviedo, Oviedo, Spain
  • Fabio Cuzzolin, Oxford Brookes University, Oxford, UK
  • Yong Deng, University of Electronic Science and Technology of China
  • Thierry Denœux, Université de Technologie de Compiègne, France
  • Sébastien Destercke, Université de Technologie de Compiègne, France
  • Jean Dezert, ONERA, Palaiseau, France
  • Didier Dubois, Toulouse Institute of Computer Science Research, Toulouse, France
  • Zied Elouedi, Institut Supérieur de Gestion de Tunis, Tunisia
  • Chao Fu, Hefei University of Technology, Hefei, China
  • Ruobin Gong, Rudgers University, USA
  • Deqiang Han, Xi’an Jiaotong University, Xi'An, China
  • Van Nam Huynh, Japan Advanced Institute of Science and Technology, Nomi, Japan
  • Radim Jiroušek, University of Economics, Prague, Czech Republic
  • Anne-Laure Jousselme, Centre for Maritime Research and Experimentation, La Spezia, Italy
  • John Klein, Université de Lille, France
  • Vaclav Kratochvil, Institute of Information Theory and Automation, CAS, Prague, Czech Republic
  • Éric Lefèvre , Université d'Artois, France
  • Xinde Li, Southeast University, Nanjing, China
  • Liping Liu, University of Akron, USA
  • Zhunga Liu, Northwestern Polytechnical University, Xian, China
  • Liyao Ma, University of Jinan, Jinan, China
  • Arnaud Martin, Université de Rennes 1, Rennes, France
  • Ryan Martin, North Carolina State University, USA
  • David Mercier, Université d'Artois, France
  • Enrique Miranda, University of Oviedo, Oviedo, Spain
  • Serafín Moral, University of Granada, Granada, Spain
  • Frédéric Pichon, Université d'Artois, France
  • Benjamin Quost, Université de Technologie de Compiègne, France
  • Emmanuel Ramasso, École nationale supérieure de mécanique et des microtechniques, France
  • Johan Schubert, Swedish Defence Research Agency, Sweden
  • Prakash Shenoy, University of Kansas, USA
  • Zhigang Su, Southeast University, Nanjing, China
  • Barbara Vantaggi, University of Rome La Sapienza, Roma, Italy
  • Jiang Wen, Northwestern Polytechnical University, Xian, China
  • Jian-Bo Yang, Manchester University, Manchester, UK
  • Zhi-jie Zhou, Rocket Force University of Engineering, Xian, China