6th International Conference on Belief Functions, October 15-19, 2021, Shanghai, China.

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

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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 15-19, 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

  • April 15 May 9: final submission deadline
  • May 31 June 25: Author notification
  • June 30 July 15: Camera-ready copy due


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 8 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 Denoeux, 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 registration fee for the onsite participants will include the following items:

We expect most keynote talks to be given onsite (and tutorials to be given online).

Online participants will be able to join and participate to the onsite event via an online platform. They benefit from a discounted registration fee, which will include the following items:

Registration feesRMBEuro
on-site student1000130
on-site faculty2000260
online student384.650
online faculty769.2100
Additional ticket for gala dinner15020
Additional ticket for welcome reception13017

Students (BFAS Grants)

The onsite and online student registration fees are reduced in comparison to the standard onsite and online registration fees. Furthermore, the Belief functions and Applications Society (BFAS) is offering a number of grants to allow students with limited funding opportunities to present their work at the conference. The grant covers the registration fee (online or onsite fee depending on the chosen mode of participation by the student). Candidates should send the following information: CV, recommendation letter from supervisor and copy of paper accepted at BELIEF 2021 to the secretary of BFAS, Dr. Anne-Laure Jousselme (


will be announced soon

Keynote speakers

Keynote speakers

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.

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.

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 vari- ables 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 Denoeux, 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
  • Deqian 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