Autor:

Wawrzeńczyk, Adam ; Mielniczuk, Jan

Współtwórca:

Witczak, Marcin - ed. ; Stetter, Ralf - ed.

Tytuł:

Revisiting strategies for fitting logistic regression for positive and unlabeled data

Podtytuł:

.

Tytuł publikacji grupowej:

AMCS, volume 32 (2022)

Temat i słowa kluczowe:

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

Abstract:

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.

Wydawca:

Zielona Góra: Uniwersytet Zielonogórski

Data wydania:

2022

Typ zasobu:

artykuł

DOI:

10.34768/amcs-2022-0022

Strony:

299-309

Źródło:

AMCS, volume 32, number 2 (2022) ; kliknij tutaj, żeby przejść

Jezyk:

eng

Licencja CC BY 4.0:

kliknij tutaj, żeby przejść

Prawa do dysponowania publikacją:

Biblioteka Uniwersytetu Zielonogórskiego