Learning from positive and unlabeled data: a survey
Learning from positive and unlabeled data: a survey
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DOI：
10.1007/s10994-020-05877-5
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发表时间：
2020-04-02
期刊：
影响因子：
7.5
通讯作者：
Davis, Jesse
中科院分区：
文献类型：
--
作者：
Bekker, Jessa;Davis, Jesse
从积极和未标记的数据或PU学习中学习是学习者只能访问积极示例和未标记数据的环境。假设未标记的数据可以包含正面和负面示例。这种设置引起了机器学习文献中日益增长的兴趣，因为这种类型的数据自然出现在医学诊断和知识库完成等应用中。本文提供了PU学习中最新技术的调查。它提出了通常在该领域中出现的七个关键研究问题，并提供了有关该领域如何解决这些问题的广泛概述。
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.