Most positive and unlabeled data is subject to selection biases. The labeled
examples can, for example, be selected from the positive set because they are
easier to obtain or more obviously positive. This paper investigates how
learning can be ena BHbled in this setting. We propose and theoretically
analyze an empirical-risk-based method for incorporating the labeling
mechanism. Additionally, we investigate under which assumptions learning is
possible when the labeling mechanism is not fully understood and propose a
practical method to enable this. Our empirical analysis supports the
theoretical results and shows that taking into account the possibility of a
selection bias, even when the labeling mechanism is unknown, improves the
trained classifiers.

本文中，我们探讨了如何在正样本和未标注样本数据集的有选择偏差中进行有监督学习，并提出了一种基于经验风险的方法来加入标签机制和解决未知标签机制的情况，实验证明，即使在未知标签机制的情况下，考虑可能存在的选择偏差也会提高分类器的训练效果。