Parametric classification with soft labels using the evidential EM algorithm: linear discriminant analysis versus logistic regression

DOI10.1007/s11634-017-0301-2zbMath1414.62265OpenAlexW2768018838MaRDI QIDQ2418335

Benjamin Quost, Shou-mei Li, Thierry Denoeux

Publication date: 3 June 2019

Published in: Advances in Data Analysis and Classification. ADAC (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11634-017-0301-2

zbMATH Keywords

Dempster-Shafer theorydiscriminant analysislogistic regressionuncertain datamachine learningbelief functionspartially supervised learning

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Pattern recognition, speech recognition (68T10) Reasoning under uncertainty in the context of artificial intelligence (68T37) Parametric inference and fuzziness (62F86)

Related Items (6)

An Evidential K-Nearest Neighbor Classifier Based on Contextual Discounting and Likelihood Maximization ⋮ Rough set-based feature selection for weakly labeled data ⋮ Harmless label noise and informative soft-labels in supervised classification ⋮ A new evidential \(K\)-nearest neighbor rule based on contextual discounting with partially supervised learning ⋮ Belief functions and rough sets: survey and new insights ⋮ On the relative value of weak information of supervision for learning generative models: an empirical study

Uses Software

• Cohn-Kanade
• mnlogit
• PRMLT

Cites Work

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