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stat.ML2024
Cost-constrained multi-label group feature selection using shadow features
Tomasz Klonecki, Paweł Teisseyre, Jaesung Lee
We consider the problem of feature selection in multi-label classification, considering the costs assigned to groups of features. In this task, the goal is to select a subset of fe…
stat.ML2024★ 1 cited
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…