Semi-supervised learning from only positive and unlabeled data using entropy

Authors:
Xiaoling Wang;Zhen Xu;Chaofeng Sha;Martin Ester;Aoying Zhou
Affiliations:
Software Engineering Institute, East China Normal University, China;Shanghai Key Lab. of Intelligent Information Processing, Fudan University, China;Shanghai Key Lab. of Intelligent Information Processing, Fudan University, China;School of Computing Science, Simon Fraser University, Burnaby, Canada;Software Engineering Institute, East China Normal University, China and Shanghai Key Lab. of Intelligent Information Processing, Fudan University, China
Venue:
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Year:
2010

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Abstract

The problem of classification from positive and unlabeled examples attracts much attention currently. However, when the number of unlabeled negative examples is very small, the effectiveness of former work has been decreased. This paper propose an effective approach to address this problem, and we firstly use entropy to selects the likely positive and negative examples to build a complete training set; and then logistic regression classifier is applied on this new training set for classification. A series of experiments are conducted. The experimental results illustrate that the proposed approach outperforms previous work in the literature.