A new evidential \(K\)-nearest neighbor rule based on contextual discounting with partially supervised learning

DOI10.1016/j.ijar.2019.07.009zbMath1468.68151OpenAlexW2965768175MaRDI QIDQ2302780

Songsak Sriboonchitta, Orakanya Kanjanatarakul, Thierry Denoeux

Publication date: 26 February 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.2019.07.009

zbMATH Keywords

classificationDempster-Shafer theoryuncertain datamachine learningbelief functionssoft labels

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Reasoning under uncertainty in the context of artificial intelligence (68T37)

Related Items (5)

Lymphoma segmentation from 3D PET-CT images using a deep evidential network ⋮ Special issue from the 5th international conference on belief functions (BELIEF 2018) ⋮ Evidential instance selection for \(K\)-nearest neighbor classification of big data ⋮ A heuristic representation learning based on evidential memberships: case study of UCI-SPECTF ⋮ Belief functions and rough sets: survey and new insights

Uses Software

• ElemStatLearn

Cites Work

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• Corrigendum to ``Belief functions contextual discounting and canonical decompositions
• An Evidential K-Nearest Neighbor Classifier Based on Contextual Discounting and Likelihood Maximization

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