Jingxiong Zhang

Accessible, Realistic, and Fair Evaluation of Positive-Unlabeled Learning Algorithms

Positive-unlabeled (PU) learning is a weakly supervised binary classification problem, in which the goal is to learn a binary classifier from only positive and unlabeled data, without access to negative data. In recent years, many PU learning algorithms have been developed to improve model performan…