BELIEF 2021: 6th International Conference on Belief Functions

Friday, October 15th, 2021 – Sunday, October 17th, 2021

* All times are in the Etc/UTC timezone.

Description:

The theory of belief functions, also known as Dempster-Shafer theory, is now well established as a general framework for reasoning with uncertainty, and has close connections to probability, possibility, and imprecise probability theories. The BELIEF conferences, sponsored by the Belief Functions and Applications Society, are the primary international forums where recent developments are shared and discussed. This year's event will feature tutorials, keynote lectures, and paper presentations. Further information is available here.

The paper presentations and keynote lectures will be hosted on Zoom. The links to the Zoom meeting room are given in the individual session pages below.

Registration fees are paid via credit card on this site -- click the "Register" button. To request a detailed receipt after registration, please fill out the form here.

A 50% student discount is available. To request a student discount code, please fill out the form here; you need this discount code before you register.

BELIEF 2021: 6th International Conference on Belief Functions

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Schedule

Epistemic random fuzzy sets: A general model of uncertainty

Introduction to information fusion in belief function theory

Improving Micro-Extended Belief Rule-based System Using Activation Factor for Classification Problems

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

Imbalance Data Classification Based on Belief Function Theory

A Classification Tree Method Based on Belief Entropy for Evidential Data

A New Multi-Source Information Fusion Method Based on Belief Divergence Measure and the Negation of Basic Probability Assignment

Improving an evidential source of information using contextual corrections depending on partial decisions

Entropy-based Learning of Compositional Models from Data

Approximately valid and model-free possibilistic inference

Ensemble learning based on evidential reasoning rule with a new weight calculation method

Towards a theory of valid inferential models with partial prior information

Machine Learning coupled with Evidential Reasoning for User Preference

Basic Utility Theory for Belief Functions

Validation of Smets’ Hypothesis in the Crowdsourcing Environment

Quantifying confidence of safety cases with belief functions

Evidential segmentation of 3D PET/CT images

Fusion of evidential CNN classifiers for image classification

Multi-branch Recurrent Attention Convolutional Neural Network with Evidence Theory for Fine-grained Image Classification

Deep Evidential Fusion Network for Image Classification

Conﬂict Measure of Belief Functions with Blurred Focal Elements on the Real Line

Logical and Evidential Inconsistencies Meet: First Steps

A note about entropy and inconsistency in evidence theory

An extension of specificity-based approximations to other belief function relations

Learning-based Modelized Methods for Evidence Combination

Nonlinear Limit Theorems Associated With Two Armed Bandit Problem

Fast Unfolding of Credal Partitions in Evidential Clustering

Credal clustering for imbalanced data

Evidential weighted multi-view clustering

Unequal Singleton Pair Distance for Evidential Preference Clustering

Transfer Evidential C-means Clustering

Evidential clustering based on transfer learning

Ensemble of Adapters for Transfer Learning Based on Evidence Theory

An efficient computation of Dempster-Shafer theory of evidence based on native GPU implementation

QLEN: Quantum-Like Evidential Networks for Predicting the Decision in Prisoner's Dilemma

Discussions on the connectedness of a random closed set