Weakly Supervised AUC Optimization: A Unified Partial AUC Approach

• URL: http://arxiv.org/abs/2305.14258v2
• Date: Wed, 27 Mar 2024 05:45:37 GMT
• Title: Weakly Supervised AUC Optimization: A Unified Partial AUC Approach
• Authors: Zheng Xie, Yu Liu, Hao-Yuan He, Ming Li, Zhi-Hua Zhou,
• Abstract summary: We present WSAUC, a unified framework for weakly supervised AUC optimization problems. We first frame the AUC optimization problems in various weakly supervised scenarios as a common formulation of minimizing the AUC risk on contaminated sets. We then introduce a new type of partial AUC, specifically, the reversed partial AUC (rpAUC), which serves as a robust training objective for AUC in the presence of contaminated labels.
• Score: 53.59993683627623
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Since acquiring perfect supervision is usually difficult, real-world machine learning tasks often confront inaccurate, incomplete, or inexact supervision, collectively referred to as weak supervision. In this work, we present WSAUC, a unified framework for weakly supervised AUC optimization problems, which covers noisy label learning, positive-unlabeled learning, multi-instance learning, and semi-supervised learning scenarios. Within the WSAUC framework, we first frame the AUC optimization problems in various weakly supervised scenarios as a common formulation of minimizing the AUC risk on contaminated sets, and demonstrate that the empirical risk minimization problems are consistent with the true AUC. Then, we introduce a new type of partial AUC, specifically, the reversed partial AUC (rpAUC), which serves as a robust training objective for AUC maximization in the presence of contaminated labels. WSAUC offers a universal solution for AUC optimization in various weakly supervised scenarios by maximizing the empirical rpAUC. Theoretical and experimental results under multiple settings support the effectiveness of WSAUC on a range of weakly supervised AUC optimization tasks.

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