An efficient semi-supervised representatives feature selection algorithm based on information theory
详细信息 查看全文
- 作者：Yintong Wanga ; b ; wangyintong@nuaa.edu.cn" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Jiandong WangbAuthor Vitae ; Hao Liaoc ; jamesliao520@gmail.com" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Haiyan ChenbAuthor Vitae
- 关键词：Feature selection ; Markov blanket ; Information theory ; Semi-supervised learning ; Representative features
- 刊名：Pattern Recognition
- 出版年：2017
- 出版时间：January 2017
- 年：2017
- 卷：61
- 期：Complete
- 页码：511-523
- 全文大小：1247 K
文摘
- •
A relevance gain framework by which the relevance of features can be measured in the unlabeled data.
- •
The partition of the directed acyclic graph to cluster the redundant features.
- •
Extend the existing Markov blanket algorithms to exploit the information of the unlabeled data.