A new hybrid feature selection approach using feature association map for supervised and unsupervised classification
作者：
Highlights：
• The algorithm visually partitions the redundant and non-redundant features.
• Visual partition enables right strategy adoption for right group of features.
• Graph-theoretic principle (vertex cover,independent set) used for subset selection.
• Algorithm applies for both supervised as well as unsupervised feature selection.
• Better results (accuracy/purity) than benchmark supervised/unsupervised algorithms.
摘要
•The algorithm visually partitions the redundant and non-redundant features.•Visual partition enables right strategy adoption for right group of features.•Graph-theoretic principle (vertex cover,independent set) used for subset selection.•Algorithm applies for both supervised as well as unsupervised feature selection.•Better results (accuracy/purity) than benchmark supervised/unsupervised algorithms.
论文关键词：Feature selection,Graph theory,Classification,Clustering
论文评审过程：Received 18 March 2017, Revised 29 May 2017, Accepted 19 June 2017, Available online 1 July 2017, Version of Record 4 July 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.06.032
