Affiliation:
1. University of Kent, Canterbury, United Kingdom
Abstract
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.
Publisher
Association for Computing Machinery (ACM)
Reference58 articles.
1. Learning from positive and unlabeled data: a survey
2. Elkan , C. and Noto , K ., 2008. Learning classifiers from only positive and unlabeled data . In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , pp. 213 -- 220 . Elkan, C. and Noto, K., 2008. Learning classifiers from only positive and unlabeled data. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 213--220.
3. Disease genes prediction by HMM based PU-learning using gene expression profiles
4. C-PUGP: A cluster-based positive unlabeled learning method for disease gene prediction and prioritization
5. Positive-unlabeled learning for disease gene identification
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