Enhancing naive classifier for positive unlabeled data based on logistic regression approach

• Autorzy: Płatek Mateusz , Mielniczuk Jan

Identyfikatory

DOI

10.15439/2023F1402

• Języki publikacji: EN

Abstrakty

EN

It is argued that for analysis of Positive Unlabeled (PU) data under Selected Completely At Random (SCAR) assumption it is fruitful to view the problem as fitting of misspecified model to the data. Namely, it is shown that the results on misspecified fit imply that in the case when posterior probability of the response is modelled by logistic regression, fitting the logistic regression to the observable PU data which does not follow this model, still yields the vector of estimated parameters approximately colinear with the true vector of parameters. This observation together with choosing the intercept of the classifier based on optimisation of analogue of F1 measure yields a classifier which performs on par or better than its competitors on several real data sets considered.

Słowa kluczowe

EN

computer science; logistic regression; fitting; estimation; size measurement; probabilistic logic; data models

PL

informatyka; regresja logistyczna; dopasowanie; estymacja; pomiar wielkości; logika probabilistyczna; modele danych

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2023
• Tom: Vol. 35
• Strony: 225--233
• Opis fizyczny: Bibliogr. 14 poz., wz., tab.

Twórcy

autor

Płatek Mateusz

• Warsaw University of Technology Faculty of Mathematics and Information Science Koszykowa 75, 00-662 Warsaw, Poland

autor

Mielniczuk Jan

• Institute of Computer Science Polish Academy of Sciences Jana Kazimierza 5, 01-248 Warsaw, Poland
• Warsaw University of Technology Faculty of Mathematics and Information Science Koszykowa 75, 00-662 Warsaw, Poland

• https://orcid.org/0000%E2%88%920003%E2%88%922621%E2%88%922303

Bibliografia

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Uwagi

1. Main Track Regular Papers

2. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).