AUC Maximization from Biased Positive-unlabeled Data with Confidence

2026-09-09

AI Summary

本文提出了一种利用少量带置信度的正样本处理偏差PU数据以最大化AUC的方法，解决了因隐私或专业标注需求导致负样本难以收集的问题。

Abstract

Maximizing the area under the receiver operating characteristic curve (AUC) is a standard approach to imbalanced binary classification. Although positive and negative data are required for maximizing the AUC, negative data are often difficult to collect in some real-world applications due to privacy concerns or the need for specialized expertise to annotate them. Thus, AUC maximization from positive and unlabeled (PU) data has been attracting attention. Existing methods assume that labeled positive data are unbiased samples from the true positive distribution. However, this ideal assumption is often violated in practice. In this paper, we propose a method to maximize the AUC from biased PU data. To address the bias, our key idea is to exploit {\it confidence}, i.e., the probability that an instance is positive, associated with the small number of labeled positive data. We derive an estimator of the AUC risk using biased PU data with confidence, enabling AUC maximization under such bias. We further show that the rewritten AUC risk induces a Bayes-optimal AUC ranking even when the available confidence is any strictly increasing transformation of the true posterior probability. We experimentally show the effectiveness of our method on eight real-world datasets.

Problem

• AUC Maximization
• Biased Positive-Unlabeled Data
• Confidence

Innovation

• AUC Maximization
• Biased Positive-Unlabeled Data
• Confidence
• Bayes-optimal AUC Ranking