An improved genetic-fuzzy system for classification and data analysis
作者：
Highlights：
• Two variant fuzzy classifiers of a well-known fuzzy classifier were proposed.
• In the first one, NSGA-II was replaced with an enhanced version.
• In the second one, solutions generated in the initial population were improved.
• The two variant fuzzy classifiers improved the accuracy-interpretability trade-off.
• The two variant classifiers can be used for both classification and data analysis.
摘要
•Two variant fuzzy classifiers of a well-known fuzzy classifier were proposed.•In the first one, NSGA-II was replaced with an enhanced version.•In the second one, solutions generated in the initial population were improved.•The two variant fuzzy classifiers improved the accuracy-interpretability trade-off.•The two variant classifiers can be used for both classification and data analysis.
论文关键词：Fuzzy rule-based systems,Interpretability,Multi-objective genetic algorithms,NSGA-II
论文评审过程：Received 8 September 2016, Revised 8 April 2017, Accepted 9 April 2017, Available online 10 April 2017, Version of Record 24 April 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.04.022
