Estimating the F1 score for learning from positive and unlabeled examples

Semi-supervised learning can be applied to datasets that contain both labeled and unlabeled instances and can result in more accurate predictions compared to fully supervised or unsupervised learning in case limited labeled data is available. A subclass of problems, called Positive-Unlabeled (PU) learning, focuses on cases in which the labeled instances contain only positive examples. Given the lack of negatively labeled data, estimating the general performance is difficult. In this paper, we propose a n