Soft Label PU Learning

arXiv:2405.01990v1 Announce Type: new
Abstract: PU learning refers to the classification problem in which only part of positive samples are labeled. Existing PU learning methods treat unlabeled samples equally. However, in many real tasks, from common sense or domain knowledge, some unlabeled samples are more likely to be positive than others. In this paper, we propose soft label PU learning, in which unlabeled data are assigned soft labels according to their probabilities of being positive. Considering that the ground truth …

abstract arxiv classification common sense cs.lg domain domain knowledge however knowledge paper part positive samples sense tasks type