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2010
Springer

Semi-supervised Learning from Only Positive and Unlabeled Data Using Entropy

9 years 11 months ago
Semi-supervised Learning from Only Positive and Unlabeled Data Using Entropy
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.
Xiaoling Wang, Zhen Xu, Chaofeng Sha, Martin Ester
Added 11 Jul 2010
Updated 11 Jul 2010
Type Conference
Year 2010
Where WAIM
Authors Xiaoling Wang, Zhen Xu, Chaofeng Sha, Martin Ester, Aoying Zhou
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