Belief Functions: Theory and Applications

Evidential Clustering A Distributional Approach for Soft Clustering Comparison and Evaluation.- Causal transfer evidential clustering.- Jiang A variational Bayesian clustering approach to acoustic emission interpretation including soft labels.- Evidential clustering by Competitive Agglomeration.- Imperfect Labels with Belief Functions for Active Learning.- Machine Learning and Pattern Recognition An Evidential Neural Network Model for Regression Based on Random Fuzzy Numbers.- Ordinal Classification using Single-model Evidential Extreme Learning Machine.- Reliability-based imbalanced data classification with Dempster-Shafer theory.- Evidential regression by synthesizing feature selection and parameters learning.- Algorithms and Evidential Operators Distributed EK-NN classification.- On improving a group of evidential sources with different contextual corrections.- Measure of Information Content of Basic Belief Assignments.- Belief functions on On Modelling and Solving the Shortest PathProblem with Evidential Weights.- Data and Information Fusion Heterogeneous Image Fusion for Target Recognition based on Evidence Reasoning.- Cluster Decomposition of the Body of Evidence.- Evidential Trustworthiness Estimation for Cooperative Perception.- An Intelligent System for Managing Uncertain Temporal Flood events.- Statistical Inference - Graphical Models A practical strategy for valid partial prior-dependent possibilistic inference.- On Conditional Belief Functions in the Dempster-Shafer Theory.- Valid inferential models offer performance and probativeness assurances.Links with Other Uncertainty Theories A qualitative counterpart of belief functions with application to uncertainty propagation in safety cases.- The Extension of Dempster's Combination Rule Based on Generalized Credal Sets.- A Correspondence between Credal Partitions and Fuzzy Orthopartitions.- Toward updating belief functions over Belnap-Dunn logic.- Applications Real bird dataset with imprecise and