Ensemble feature selection using bi-objective genetic algorithm
作者：
Highlights：
• An ensemble parallel processing bi-objective genetic algorithm based feature selection method is proposed.
• Rough set theory and Mutual information gain are used to select informative data removing the vague one.
• Parallel processing in genetic algorithm reduces time complexity.
• The method is compared with the existing state-of-the-art methods using suitable datasets.
• Classification accuracy and statistical measures outperforms that of other state-of-the-art methods.
摘要
•An ensemble parallel processing bi-objective genetic algorithm based feature selection method is proposed.•Rough set theory and Mutual information gain are used to select informative data removing the vague one.•Parallel processing in genetic algorithm reduces time complexity.•The method is compared with the existing state-of-the-art methods using suitable datasets.•Classification accuracy and statistical measures outperforms that of other state-of-the-art methods.
论文关键词：Feature selection,Genetic algorithm,Rough set theory,Mutual information,Evolutionary optimization,Supervised learning
论文评审过程：Received 17 August 2016, Revised 6 January 2017, Accepted 10 February 2017, Available online 14 February 2017, Version of Record 27 March 2017.
论文官网地址：https://doi.org/10.1016/j.knosys.2017.02.013
