Heterogeneous information fusion: combination of multiple supervised and unsupervised classification methods based on belief functions

DOI10.1016/j.ins.2020.07.039zbMath1475.68281OpenAlexW3043653956MaRDI QIDQ2054083

Arnaud Martin, Rémi Estival, Na Li

Publication date: 30 November 2021

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2020.07.039

zbMATH Keywords

belief functionscombination of classification and clustering methodsheterogeneous information fusion

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (1)

Uncertain knowledge representation and reasoning with linguistic belief structures

Cites Work

• Belief functions: The disjunctive rule of combination and the generalized Bayesian theorem
• Multigranulation information fusion: a Dempster-Shafer evidence theory-based clustering ensemble method
• Conjunctive and disjunctive combination of belief functions induced by nondistinct bodies of evidence
• The combination of multiple classifiers using an evidential reasoning approach
• From model-based control to data-driven control: survey, classification and perspective
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