PUe: Biased Positive-Unlabeled Learning Enhancement by Causal Inference

• URL: http://arxiv.org/abs/2607.13428v1
• Date: Wed, 15 Jul 2026 04:12:30 GMT
• Title: PUe: Biased Positive-Unlabeled Learning Enhancement by Causal Inference
• Abstract summary: Positive-Unlabeled (PU) learning aims to achieve high-accuracy binary classification with limited labeled positive examples and numerous unlabeled ones.<n>We study a PU learning enhancement framework using normalized propensity scores and normalized inverse probability weighting.<n> Experiments on MNIST, CIFAR-10, and ADNI demonstrate improvements over several PU baselines under non-uniform label distributions.
• Score: 30.925593450717702
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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