Learning to improve AUC performance is an important topic in machine learning. However, AUC maximization algorithms may decrease generalization performance due to the noisy data. Self-paced learning is an effective method for handling noisy data. However, existing self-paced learning methods are limited to pointwise learning, while AUC maximization is a pairwise learning problem. To solve this challenging problem, we innovatively propose a balanced self-paced AUC maximization algorithm (BSPAUC). Specifically, we first provide a statistical objective for self-paced AUC. Based on this, we propose our self-paced AUC maximization formulation, where a novel balanced self-paced regularization term is embedded to ensure that the selected positive and negative samples have proper proportions. Specially, the sub-problem with respect to all weight variables may be non-convex in our formulation, while the one is normally convex in existing self-paced problems. To address this, we propose a doubly cyclic block coordinate descent method. More importantly, we prove that the sub-problem with respect to all weight variables converges to a stationary point on the basis of closed-form solutions, and our BSPAUC converges to a stationary point of our fixed optimization objective under a mild assumption. Considering both the deep learning and kernel-based implementations, experimental results on several large-scale datasets demonstrate that our BSPAUC has a better generalization performance than existing state-of-the-art AUC maximization methods.

本文提出了一种平衡的自适应正类比例（BSPAUC）算法来解决在最大化AUC时存在的噪声数据问题，该算法通过嵌入一个新颖的平衡自适应正则化项，使得选择的正类和负类样本具有适当的比例，并通过解决非凸子问题所面临的挑战来达到更好的泛化性能，在深度学习和基于核的实现中，实验结果在多个大型数据集上证明了BSPAUC具有比现有的最新AUC最大化方法更好的泛化性能。

AUC最大化的平衡自适应学习