论文标题
评估肯定分类器的预测性能：简短的批判性审查和改进的实用建议
Evaluating the Predictive Performance of Positive-Unlabelled Classifiers: a brief critical review and practical recommendations for improvement
论文作者
论文摘要
积极的无标记（PU）学习是机器学习的增长领域，旨在从由标记的正面和未标记实例组成的数据中学习分类器。尽管已经完成了用于PU学习的方法的许多工作，但关于评估这些方法的主题几乎没有写过。由于没有完全标记的数据，因此无法精确计算许多流行的标准分类指标，因此必须采用替代方法。这篇简短的评论论文批判性地回顾了主要的PU学习评估方法以及在51篇文章中选择PU分类器的预测精度度量，并为改进该领域提供了实用建议。
Positive-Unlabelled (PU) learning is a growing area of machine learning that aims to learn classifiers from data consisting of labelled positive and unlabelled instances. Whilst much work has been done proposing methods for PU learning, little has been written on the subject of evaluating these methods. Many popular standard classification metrics cannot be precisely calculated due to the absence of fully labelled data, so alternative approaches must be taken. This short commentary paper critically reviews the main PU learning evaluation approaches and the choice of predictive accuracy measures in 51 articles proposing PU classifiers and provides practical recommendations for improvements in this area.
