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stat.ML2023
Enhancing naive classifier for positive unlabeled data based on logistic regression approach
Mateusz Płatek, Jan Mielniczuk
We argue 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…
stat.ML2022★ 2 cited
Double logistic regression approach to biased positive-unlabeled data
Konrad Furmańczyk, Jan Mielniczuk, Wojciech Rejchel +1
Positive and unlabelled learning is an important problem which arises naturally in many applications. The significant limitation of almost all existing methods lies in assuming tha…
stat.ML2013★ 13 cited
Combined l_1 and greedy l_0 penalized least squares for linear model selection
Piotr Pokarowski, Jan Mielniczuk
We introduce a computationally effective algorithm for a linear model selection consisting of three steps: screening--ordering--selection (SOS). Screening of predictors is based on…