Joint feature selection and classification for positive unlabelled multi-label data using weighted penalized empirical risk minimization

DOI10.34768/amcs-2022-0023OpenAlexW4378631623MaRDI QIDQ2162144

Paweł Teisseyre

Publication date: 5 August 2022

Published in: International Journal of Applied Mathematics and Computer Science (Search for Journal in Brave)

Full work available at URL: https://doaj.org/article/d6baab7d9d5d48daa28b1314ab63ad84

zbMATH Keywords

feature selectionmulti-label classificationempirical risk minimizationpositive and unlabelled data

Mathematics Subject Classification ID

Artificial intelligence (68Txx) Multivariate analysis (62Hxx) Statistical sampling theory and related topics (62Dxx)

Related Items (1)

FSPL: a meta-learning approach for a filter and embedded feature selection pipeline

Uses Software

• MULAN
• ElemStatLearn
• DALEX

Cites Work

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• Class-prior estimation for learning from positive and unlabeled data
• Dealing with under-reported variables: an information theoretic solution
• Estimating the class prior for positive and unlabelled data via logistic regression
• 10.1162/153244303322753616
• Maximum Likelihood Estimation and Coarse Data
• Understanding Machine Learning

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