1 citations · 2 across the 4 of their papers we have counts for
4 papers
Augmented prediction of a true class for Positive Unlabeled data under selection bias
Jan Mielniczuk, Adam Wawrzeńczyk
We introduce a new observational setting for Positive Unlabeled (PU) data where the observations at prediction time are also labeled. This occurs commonly in practice -- we argue t…
Verifying the Selected Completely at Random Assumption in Positive-Unlabeled Learning
Paweł Teisseyre, Konrad Furmańczyk, Jan Mielniczuk
The goal of positive-unlabeled (PU) learning is to train a binary classifier on the basis of training data containing positive and unlabeled instances, where unlabeled observations…
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…
Linear regression model selection using p-values when the model dimension grows
Piotr Pokarowski, Jan Mielniczuk, Paweł Teisseyre
We consider a new criterion-based approach to model selection in linear regression. Properties of selection criteria based on p-values of a likelihood ratio statistic are studied f…