Revisiting strategies for fitting logistic regression for positive and unlabeled data

• Autorzy: Wawrzeńczyk Adam , Mielniczuk Jan

Identyfikatory

DOI

10.34768/amcs-2022-0022

• Języki publikacji: EN

Abstrakty

EN

Positive unlabeled (PU) learning is an important problem motivated by the occurrence of this type of partial observability in many applications. The present paper reconsiders recent advances in parametric modeling of PU data based on empirical likelihood maximization and argues that they can be significantly improved. The proposed approach is based on the fact that the likelihood for the logistic fit and an unknown labeling frequency can be expressed as the sum of a convex and a concave function, which is explicitly given. This allows methods such as the concave-convex procedure (CCCP) or its variant, the disciplined convex-concave procedure (DCCP), to be applied. We show by analyzing real data sets that, by using the DCCP to solve the optimization problem, we obtain significant improvements in the posterior probability and the label frequency estimation over the best available competitors.

Słowa kluczowe

EN

positive learning; unlabeled learning; empirical risk; logistic regression; concave convex optimization

PL

pozytywne uczenie się; nieoznaczone uczenie się; ryzyko empiryczne; regresja logistyczna

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2022
• Tom: Vol. 32, no. 2
• Strony: 299--309
• Opis fizyczny: Bibliogr. 17 poz., tab., wykr.

Twórcy

autor

Wawrzeńczyk Adam

• Institute of Computer Science, Polish Academy of Sciences, Jana Kazimierza 5, 01-248 Warsaw, Poland

autor

Mielniczuk Jan

• Institute of Computer Science, Polish Academy of Sciences, Jana Kazimierza 5, 01-248 Warsaw, Poland
• Faculty of Mathematics and Information Sciences, Warsaw University of Technology, Koszykowa 5, 00-662 Warsaw, Poland

Bibliografia

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