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

摘要：

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 cffective approach to address this problem, and we firstly use entropy to selects the likely pos itive 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.

作者: Xiaoling Wang Zhen Xu Chaofeng Sha Martin Ester Aoying Zhou

作者单位: Software Engineering Institute, East China Normal 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 Shanghai Key Lab. of Intelligent

会议类型: 国际会议

会议名称: 11th International Conference,WAIM 2010(第十一届网络时代管理国际会议)

会议地点: 九寨沟

会议语种:英文

页码: 668-679

在线出版日期: 2010-07-14（万方平台首次上网日期，不代表论文的发表时间）