Belief Functions: Theory and Applications

8th International Conference, BELIEF 2024, Belfast, UK, September 2-4, 2024, Proceedings
Herausgegeben:Bi, Yaxin; Jousselme, Anne-Laure; Denoeux, Thierry

• Broschiertes Buch

Produktbeschreibung

This book constitutes the refereed proceedings of the 8th International Conference on Belief Functions, BELIEF 2024, held in Belfast, UK, in September 2-4, 2024.

The 30 full papers presented in this book were carefully selected and reviewed from 36 submissions. The papers cover a wide range on theoretical aspects on Machine learning; Statistical inference; Information fusion and optimization; Measures of uncertainty, conflict and distances; Continuous belief functions, logics, computation.

Produktdetails

• Lecture Notes in Computer Science 14909
• Verlag: Springer / Springer Nature Switzerland / Springer, Berlin
• Artikelnr. des Verlages: 978-3-031-67976-6
• 2024
• Seitenzahl: 308
• Erscheinungstermin: 20. August 2024
• Englisch
• Abmessung: 235mm x 155mm x 17mm
• Gewicht: 470g
• ISBN-13: 9783031679766
• ISBN-10: 3031679768
• Artikelnr.: 71184601

Inhaltsangabe

.- Machine learning.

.- Deep evidential clustering of images.

.- Incremental Belief-peaks Evidential Clustering.

.- Imprecise Deep Networks for Uncertain Image Classification.

.- Dempster-Shafer Credal Probabilistic Circuits.

.- Uncertainty quantification in regression neural networks using likelihood-based belief functions.

.- An evidential time-to-event prediction model based on Gaussian random fuzzy numbers.

.- Object Hallucination Detection in Large Vision Language Models via Evidential Conflict.

.- Multi-oversampling with evidence fusion for imbalanced data classification.

.- An Evidence-based Framework For Heterogeneous Electronic Health Records: A Case Study In Mortality Prediction.

.- Conflict Management in a Distance to Prototype-Based Evidential Deep Learning.

.- A Novel Privacy Preserving Framework for Training Dempster-Shafer Theory-based Evidential Deep Neural Network.

.- Statistical inference.

.- Large-sample theory for inferential models: A possibilistic Bernstein-von Mises theorem.

.- Variational approximations of possibilistic inferential models.

.- Decision theory via model-free generalized fiducial inference.

.- Which statistical hypotheses are afflicted with false confidence?.

.- Algebraic expression for the relative likelihood-based evidential prediction of an ordinal variable.

.- Information fusion and optimization.

.- Why Combining Belief Functions on Quantum Circuits?.

.- SHADED: Shapley Value-based Deceptive Evidence Detection in Belief Functions.

.- A Novel Optimization-Based Combination Rule for Dempster-Shafer Theory.

.- Fusing independent inferential models in a black-box manner.

.- Optimization under Severe Uncertainty: a Generalized Minimax Regret Approach for Problems with Linear Objectives.

.- Measures of uncertainty, conflict and distances.

.- A mean distance between elements of same class for rich labels.

.- Threshold Functions and Operations in the Theory of Evidence.

.- Mutual Information and Kullback-Leibler Divergence in the Dempster-Shafer Theory.

.- An OWA-based Distance Measure for Ordered Frames of Discernment.

.- Automated Hierarchical Conflict Reduction for Crowdsourced Annotation Tasks using Belief Functions.

.- Continuous belief functions, logics, computation.

.- Gamma Belief Functions.

.- Combination of Dependent Gaussian Random Fuzzy Numbers.

.- A 3-valued Logical Foundation for Evidential Reasoning.

.- Accelerated Dempster Shafer using Tensor Train Representation.