Learning from positive and unlabeled data or PU learning is the setting where a learner only has access to positive examples and unlabeled data. The assumption is that the unlabeled data can contain both positive and negative examples. This setting has attracted increasing interest within the machine learning literature as this type of data naturally arises in applications such as medical diagnosis and knowledge base completion. This article provides a survey of the current state of the art in PU learning. It proposes seven key research questions that commonly arise in this field and provides a broad overview of how the field has tried to address them.

中文翻译：

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从正面和未标记的数据中学习：一项调查

从正面和未标记数据或 PU 学习中学习是学习者只能访问正面示例和未标记数据的设置。假设是未标记的数据可以包含正例和负例。这种设置在机器学习文献中引起了越来越多的兴趣，因为这种类型的数据自然会出现在医疗诊断和知识库完成等应用中。本文提供了对 PU 学习当前技术状态的调查。它提出了该领域中常见的七个关键研究问题，并提供了该领域如何尝试解决这些问题的广泛概述。