Belief Functions: Theory and Applications : 7th International Conference, BELIEF 2022, Paris, France, October 26–28, 2022, Proceedings / edited by Sylvie Le Hégarat-Mascle, Isabelle Bloch, Emanuel Aldea.

Format:

Book

Contributor:

Bloch, Isabelle, editor.

Aldea, Emanuel, editor.

Le Hégarat-Mascle, Sylvie, editor.

Series:

Lecture Notes in Artificial Intelligence, 2945-9141 ; 13506

Language:

English

Subjects (All):

Probabilities.

Probability Theory.

Local Subjects:

Probability Theory.

Physical Description:

1 online resource (318 pages)

Edition:

1st ed. 2022.

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2022.

System Details:

Mode of access: World Wide Web.

Summary:

This book constitutes the refereed proceedings of the 7th International Conference on Belief Functions, BELIEF 2022, held in Paris, France, in October 2022. 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 pattern recognition. The 29 full papers presented in this book were carefully selected and reviewed from 31 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.

Contents:

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 uncertainvalues

Addressing ambiguity in randomized reinsurance contracts using belief functions

Evidential filtering and spatio-temporal gradient for micro-movements analysis in the context of bedsores prevention

Hybrid Artificial Immune Recognition System with improved belief classification process.

Notes:

Includes bibliographical references and index.

Other Format:

Print version: Le Hégarat-Mascle, Sylvie Belief Functions: Theory and Applications

ISBN:

9783031178016

3031178017