An efficient semi-supervised representatives feature selection algorithm based on information theory
作者：
Highlights：
• 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.
摘要
Highlights•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.
论文关键词：Feature selection,Markov blanket,Information theory,Semi-supervised learning,Representative features
论文评审过程：Received 18 January 2015, Revised 24 May 2016, Accepted 12 August 2016, Available online 13 August 2016, Version of Record 2 September 2016.
论文官网地址：https://doi.org/10.1016/j.patcog.2016.08.011
