PUe：基于因果推断的有偏正无标记学习增强
PUe: Biased Positive-Unlabeled Learning Enhancement by Causal Inference
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中文总结 AI 辅助
研究正无标记学习问题，基于SAR-PU倾向加权框架提出PUe框架，运用归一化倾向得分和NIPW，有归一化逆概率加权风险公式等贡献，在多个数据集实验中，在非均匀标签分布下优于多个PU基线。
中文摘要 AI 辅助
正无标记（PU）学习旨在利用有限的标记正例和大量未标记例实现高精度二分类。现有基于成本敏感的方法常依赖强假设，即观察到正标记的示例是完全随机选择的。但实际中标签分布不均，存在选择偏差。基于Bekker等人的SAR-PU倾向加权框架，研究使用归一化倾向得分和归一化逆概率加权（NIPW）的PU学习增强（PUe）框架。其主要贡献包括归一化逆概率加权的PU风险公式、偏差标记下归一化样本权重误差和常见PU估计器的理论分析、正则化深度倾向得分估计、与现代成本敏感PU方法集成以及对选择性标记负类的支持。在MNIST、CIFAR-10和ADNI上的实验表明，在非均匀标签分布下优于多个PU基线。
英文摘要
Positive-Unlabeled (PU) learning aims to achieve high-accuracy binary classification with limited labeled positive examples and numerous unlabeled ones. Existing cost-sensitive-based methods often rely on strong assumptions that examples with an observed positive label were selected entirely at random. In fact, the uneven distribution of labels is prevalent in real-world PU problems, indicating that most actual positive and unlabeled data are subject to selection bias. Building on the SAR-PU propensity-weighted framework of Bekker et al., we study a PU learning enhancement (PUe) framework using normalized propensity scores and normalized inverse probability weighting (NIPW). PUe's main contributions are a normalized inverse-probability-weighted PU risk formulation; additional theoretical analyses of normalized sample-weight error and common PU estimators under biased labeling; regularized deep propensity-score estimation; integration with modern cost-sensitive PU methods; and support for selectively labeled negative classes. Experiments on MNIST, CIFAR-10, and ADNI demonstrate improvements over several PU baselines under non-uniform label distributions.
发表机构
- Huawei Noah’s Ark Lab（华为诺亚方舟实验室）
机构由 AI 辅助整理，请以论文原文为准。
