山西大学学报（自然科学版）2024，Vol.47Issue(1)：93-102,10.DOI:10.13451/j.sxu.ns.2023143
基于Stacking集成和偏探索贝叶斯优化的特征选择
Feature Selection Using Stacking Integration and Partial Exploration Bayesian Optimization
摘要
Abstract
To address the problems that the optimal feature subset of high-dimensional gene datasets is not easy to be determined and the traditional Bayesian optimization algorithm is prone to falling into local optimum,which cannot quickly select the optimal pa-rameters,in this paper,we propose a gene selection method based on the Stacking integration and partial exploration Bayesian opti-mization.Firstly,the Chi-square filtering scheme is used to eliminate the redundant genes in the original feature space,so as to ob-tain the genes with high correlation.The acquisition function of the Bayesian optimization algorithm is improved,and the jump out coefficient is introduced,so that the Bayesian optimization algorithm can adaptively jump out of the local optimum.The cost can be reduced and the efficiency of optimization will be speeded up.Secondly,the partial exploration Bayesian optimization is used to find the optimal parameters of random forest.Then,the optimized random forest model is employed to screen the optimal feature subset.Finally,a framework of the Stacking integration model is designed to construct classifier and classify the optimal feature subset,and then a gene selection algorithm based on the Stacking integration and partial exploration Bayesian optimization is constructed.The experimental results on nine public gene expression profile datasets show that the proposed algorithm can quickly select the optimal gene subset with higher classification accuracy.关键词
基因选择/Stacking算法/贝叶斯优化算法/随机森林模型Key words
gene selection/stacking algorithm/bayesian optimization algorithm/random forest model分类
信息技术与安全科学引用本文复制引用
孙林,郭嘉琪,朱雨晨,陈森..基于Stacking集成和偏探索贝叶斯优化的特征选择[J].山西大学学报（自然科学版）,2024,47(1):93-102,10.基金项目
国家自然科学基金(61772176) （61772176）
河南省科技攻关项目(212102210136) （212102210136）
