Maximizing the area under the receiver operating characteristic curve (AUC) is a standard approach to imbalanced classification. So far, various supervised AUC optimization methods have been developed and they are also extended to semi-supervised scenarios to cope with small sample problems. However, existing semi-supervised AUC optimization methods rely on strong distributional assumptions, which are rarely satisfied in real-world problems. In this paper, we propose a novel semi-supervised AUC optimization method that does not require such restrictive assumptions. We first develop an AUC optimization method based only on positive and unlabeled data (PU-AUC) and then extend it to semi-supervised learning by combining it with a supervised AUC optimization method. We theoretically prove that, without the restrictive distributional assumptions, unlabeled data contribute to improving the generalization performance in PU and semi-supervised AUC optimization methods. Finally, we demonstrate the practical usefulness of the proposed methods through experiments.

本文提出了一种新的半监督AUC优化方法，不需要强限制假设，其基于正例和未标记数据的AUC优化方法（PU-AUC），并将其与监督AUC优化方法结合来实现半监督学习，理论证明了未标记数据对于PU和半监督AUC优化方法的泛化性能的改善有帮助，并通过实验证明了所提出方法的实用性。

基于正类未标记学习的半监督AUC优化