Fast multi-label feature selection based on information-theoretic feature ranking
作者：
Highlights：
• A score function from mutual information between a feature and labels was derived.
• Unnecessary computations from the score function were discarded.
• A strategy to identify important labels from sparse label set was proposed.
• The computational cost of each component was analyzed theoretically.
摘要
Highlights•A score function from mutual information between a feature and labels was derived.•Unnecessary computations from the score function were discarded.•A strategy to identify important labels from sparse label set was proposed.•The computational cost of each component was analyzed theoretically.
论文关键词：Multi-label feature selection,Mutual information,Interaction information,Entropy
论文评审过程：Received 30 June 2014, Revised 9 March 2015, Accepted 5 April 2015, Available online 15 April 2015, Version of Record 16 May 2015.
论文官网地址：https://doi.org/10.1016/j.patcog.2015.04.009
