Rethinking Class-Prior Estimation for Positive-Unlabeled Learning

TL;DR

This paper introduces ReCPE, a novel method for positive-unlabeled learning that removes the support containment assumption in class-prior estimation, improving accuracy across various datasets.

Contribution

ReCPE is a new approach that enhances existing CPE methods by removing the support containment assumption, making PU learning more robust.

Findings

ReCPE improves all tested state-of-the-art CPE methods.

ReCPE reduces positive bias when the support assumption is violated.

Empirical results show consistent performance gains across datasets.

Abstract

Given only positive (P) and unlabeled (U) data, PU learning can train a binary classifier without any negative data. It has two building blocks: PU class-prior estimation (CPE) and PU classification; the latter has been well studied while the former has received less attention. Hitherto, the distributional-assumption-free CPE methods rely on a critical assumption that the support of the positive data distribution cannot be contained in the support of the negative data distribution. If this is violated, those CPE methods will systematically overestimate the class prior; it is even worse that we cannot verify the assumption based on the data. In this paper, we rethink CPE for PU learning-can we remove the assumption to make CPE always valid? We show an affirmative answer by proposing Regrouping CPE (ReCPE) that builds an auxiliary probability distribution such that the support of the positive data distribution is never contained in the support of the negative data distribution. ReCPE can work with any CPE method by treating it as the base method. Theoretically, ReCPE does not affect its base if the assumption already holds for the original probability distribution; otherwise, it reduces the positive bias of its base. Empirically, ReCPE improves all state-of-the-art CPE methods on various datasets, implying that the assumption has indeed been violated here.