Enhancing naive classifier for positive unlabeled data based on logistic regression approach
Mateusz Płatek,
Jan Mielniczuk
Abstract: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 coli… Show more
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