- Tytuł:
- Revisiting strategies for fitting logistic regression for positive and unlabeled data
- Autorzy:
-
Wawrzeńczyk, Adam
Mielniczuk, Jan - Powiązania:
- https://bibliotekanauki.pl/articles/2142489.pdf
- Data publikacji:
- 2022
- Wydawca:
- Uniwersytet Zielonogórski. Oficyna Wydawnicza
- Tematy:
-
positive learning
unlabeled learning
empirical risk
logistic regression
concave convex optimization
pozytywne uczenie się
nieoznaczone uczenie się
ryzyko empiryczne
regresja logistyczna - Opis:
- 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.
- Źródło:
-
International Journal of Applied Mathematics and Computer Science; 2022, 32, 2; 299--309
1641-876X
2083-8492 - Pojawia się w:
- International Journal of Applied Mathematics and Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł