An Information-Based Elite-Guided Evolutionary Algorithm for Multi-Objective Feature Selection

被引：0
|
作者：
Ziqian Wang [1 ]
Shangce Gao [2 ,1 ]
Zhenyu Lei [1 ]
Masaaki Omura [1 ]
机构：
[1] the Faculty of Engineering, University of Toyama
[2] IEEE
来源：
IEEE/CAA Journal of Automatica Sinica | 2024年 / 11卷 / 01期
基金：
日本科学技术振兴机构; 日本学术振兴会;
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Dear Editor, This letter is concerned with the evolution strategy for addressing multi-objective feature selection problems in classification. Previous methods suffer from limitations such as being trapped in local optima and lacking stability. To overcome them, we propose a novel eliteguided mechanism based on information theory. Firstly, an elite solution is generated through a dimension reduction strategy and incorporated to the initialization population. Then,
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页码：264 / 266
页数：3
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