计算机应用与软件2024，Vol.41Issue(5)：274-285,12.DOI:10.3969/j.issn.1000-386x.2024.05.041
基于自学习二元差分进化的多目标特征选择
MULTIPLE OBJECTIVE FEATURE SELECTION BASED ON SELF-LEARNING BINARY DIFFERENTIAL EVOLUTION
摘要
Abstract
In order to improve the group search ability and accelerate the convergence speed,a multiple objective feature selection method based on self-learning binary differential evolution is proposed.Three operators were introduced,and the binary mutation operator based on probability difference was used to generate the optimal solution,so as to quickly guide individuals to locate the potential optimal region.The clean search operator was introduced to improve the self-learning ability of the elites in the optimal region,while the non-dominated sorting operator with crowding distance could reduce the computational complexity of the selection operator in differential evolution.The experimental results on multiple data sets show that the proposed method is efficient and accurate on multiple objective feature selection.关键词
自学习/二元差分/多目标/特征选择Key words
Self learning/Binary difference/Multiple objective/Feature selection分类
信息技术与安全科学引用本文复制引用
胡振稳,杨改贞..基于自学习二元差分进化的多目标特征选择[J].计算机应用与软件,2024,41(5):274-285,12.基金项目
湖北省教育科学规划项目(2018GB064). （2018GB064）
