Verifying the Selected Completely at Random Assumption in Positive-Unlabeled Learning

arXiv:2404.00145v1 Announce Type: cross
Abstract: 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 can belong either to the positive class or to the negative class. Modeling PU data requires certain assumptions on the labeling mechanism that describes which positive observations are assigned a label. The simplest assumption, considered in early works, is SCAR (Selected Completely at Random Assumption), according to which the …

abstract arxiv assumptions binary class classifier cs.lg data instances modeling negative positive random stat.ml train training training data type