Positive-unlabeled (PU) learning trains a binary classifier using only
positive and unlabeled data. A common simplifying assumption is that the
positive data is representative of the target positive class. This assumption
rarely holds in practice due to temporal drift, domain shift, and/or
adversarial manipulation. This paper shows that PU learning is possible even
with arbitrarily non-representative positive data given unlabeled data from the
source and target distributions. Our key insight is that only the negative
class's distribution need be fixed. We integrate this into two statistically
consistent methods to address arbitrary positive bias - one approach combines
negative-unlabeled learning with unlabeled-unlabeled learning while the other
uses a novel, recursive risk estimator. Experimental results demonstrate our
methods' effectiveness across numerous real-world datasets and forms of
positive bias, including disjoint positive class-conditional supports.
Additionally, we propose a general, simplified approach to address PU risk
estimation overfitting.

本文介绍了使用正样本和未标注样本进行 PU 学习的方法，即使正样本在数据分布中不具有代表性，也可以通过只固定负类分布来解决正偏差问题。通过将负 - 未标注学习与未标注 - 未标注学习相结合或利用递归风险估计方法，可以解决正偏差问题。然后，作者提出一种通用的方法来解决 PU 风险评估的过度配合问题。实验结果表明，无论数据集如何，这两种方法都是有效的。