Class-Imbalanced Complementary-Label Learning via Weighted Loss

Complementary-label learning (CLL) is a common application in the scenario of weak supervision. However, in real-world datasets, CLL encounters class-imbalanced training samples, where the quantity of samples of one class is significantly lower than those of other classes. Unfortunately, existing CLL approaches have yet to explore the problem of class-imbalanced samples, which reduces the prediction accuracy, especially in imbalanced classes. In this paper, we propose a novel problem setting to allow learning from class-imbalanced complementarily labeled samples for multi-class classification. Accordingly, to deal with this novel problem, we propose a new CLL approach, called Weighted Complementary-Label Learning (WCLL). The proposed method models a weighted empirical risk minimization loss by utilizing the class-imbalanced complementarily labeled information, which is also applicable to multi-class imbalanced training samples. Furthermore, the estimation error bound of the proposed method was derived to provide a theoretical guarantee. Finally, we do extensive experiments on widely-used benchmark datasets to validate the superiority of our method by comparing it with existing state-of-the-art methods.