Integration of graph clustering with ant colony optimization for feature selection
作者：
Highlights：
• A novel supervised filter-based feature selection method using ACO is proposed.
• Our method integrates graph clustering with a modified ant colony search process.
• Each feature set is evaluated using a novel measure without using any learning model.
• The sizes of the final feature set is determined automatically.
• The method is compared to the state-of-the-art filter and wrapper based methods.
摘要
•A novel supervised filter-based feature selection method using ACO is proposed.•Our method integrates graph clustering with a modified ant colony search process.•Each feature set is evaluated using a novel measure without using any learning model.•The sizes of the final feature set is determined automatically.•The method is compared to the state-of-the-art filter and wrapper based methods.
论文关键词：Feature selection,Ant colony optimization,Filter method,Graph clustering
论文评审过程：Received 18 August 2014, Revised 16 January 2015, Accepted 6 April 2015, Available online 9 April 2015, Version of Record 13 May 2015.
论文官网地址：https://doi.org/10.1016/j.knosys.2015.04.007
