A heuristic representation learning based on evidential memberships: case study of UCI-SPECTF

DOI10.1016/j.ijar.2020.02.002zbMath1433.68341OpenAlexW3006918348MaRDI QIDQ2310294

Yu-Chien Ko, Hamido Fujita

Publication date: 6 April 2020

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ijar.2020.02.002

zbMATH Keywords

machine learninginformation aggregationgranulesevidential membershipheuristic representation learning

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Biomedical imaging and signal processing (92C55) Reasoning under uncertainty in the context of artificial intelligence (68T37) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Related Items (6)

An improved belief \(\chi^2\) divergence for Dempster-Shafer theory and its applications in pattern recognition ⋮ Earth mover's divergence of belief function ⋮ Entropy measure for orderable sets ⋮ A fuzzy preference-based Dempster-Shafer evidence theory for decision fusion ⋮ A generalized Rényi divergence for multi-source information fusion with its application in EEG data analysis ⋮ A generalized Hellinger distance for multisource information fusion and its application in pattern classification

Uses Software

• UCI-ml
• C4.5

Cites Work

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• Decision-making with belief functions: a review
• Rough approximation by dominance relations
• Coding Local and Global Binary Visual Features Extracted From Video Sequences
• Rough sets theory for multicriteria decision analysis
• Handling possibilistic labels in pattern classification using evidential reasoning.

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