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In | In [[machine learning]], '''PU learning''' is a collection of [[Semi-supervised learning|semisupervised]] techniques for training [[Statistical classification|binary classifiers]] on '''p'''ositive and '''u'''nlabeled examples only.<ref>{{cite book | ||
|last=Liu | |||
|first=Bing | |||
|title=Web Data Mining | |||
|publisher=Springer | |||
|year=2007 | |||
|pages=165−178 | |||
}}</ref> | |||
In PU learning, two sets of samples are assumed to be available for training: the positive set <math>P</math> and a ''mixed set'' <math>U</math>, which is assumed to contain both positive and negative samples, but without these being labeled as such. This contrasts with other forms of semisupervised learning, where it is assumed that a labeled set containing examples of both classes is available. A variety of techniques exist to adapt [[supervised learning|supervised]] classifiers to the PU learning setting. PU learning successfully been applied to [[text classification]] <ref>{{cite conference |authors=Bing Liu, Wee Sun Lee, [[Philip S. Yu]] and Xiao-Li Li |year=2002 |title=Partially supervised classification of text documents |conference=ICML |pages=8–12}}</ref><ref>{{cite conference |authors=Hwanjo Yu, Jiawei Han, Kevin Chen-Chuan Chang |title=PEBL: positive example based learning for web page classification using SVM |conference=ACM SIGKDD |year=2002}}</ref><ref>{{cite conference |authors=Xiao-Li Li and Bing Liu |title=Learning to classify text using positive and unlabeled data |conference=IJCAI |year=2003}}</ref> and [[Bioinformatics]] tasks.<ref>{{cite conference |authors=Peng Yang, Xiao-Li Li, Jian-Ping Mei, Chee-Keong Kwoh and See-Kiong Ng |title=Positive-Unlabeled Learning for Disease Gene Identification |conference=Bioinformatics, Vol 28(20)|year=2012}}</ref> | |||
==References== | |||
<references/> | |||
[[Category:Machine learning]] | |||
{{compu-sci-stub}} | |||
Revision as of 09:14, 22 March 2013
In machine learning, PU learning is a collection of semisupervised techniques for training binary classifiers on positive and unlabeled examples only.[1]
In PU learning, two sets of samples are assumed to be available for training: the positive set and a mixed set , which is assumed to contain both positive and negative samples, but without these being labeled as such. This contrasts with other forms of semisupervised learning, where it is assumed that a labeled set containing examples of both classes is available. A variety of techniques exist to adapt supervised classifiers to the PU learning setting. PU learning successfully been applied to text classification [2][3][4] and Bioinformatics tasks.[5]
References
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