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,

引用

页码：264 / 266

页数：3

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