吉林大学学报（理学版）2017，Vol.55Issue(4)：933-939,7.DOI:10.13413/j.cnki.jdxblxb.2017.04.27
基于互信息和随机森林的混合变量选择算法
Hybrid Variable Selection Algorithm Based on Mutual Information and Random Forest
摘要
Abstract
Aiming at the problem that the classification accuracy and generalization ability of model were low in single variable selection algorithms,we proposed a hybrid variable selection algorithm.The algorithm was divided into two stages.In filtration stage,mutual information was used to quickly exclude a part of irrelevant variables,which reduced the dimension of sample space.In wrapper stage,the random forest was used to refine the remaining variables in the framework of permutation theory.The experimental results show that,compared with the contrast algorithm,this algorithm has higher classification accuracy and generalization ability.关键词
变量选择/互信息/随机森林/混合算法Key words
variable selection/mutual information/random forest/hybrid algorithm分类
信息技术与安全科学引用本文复制引用
赵伟卫,李艳颖,赵风芹,魏洒洒..基于互信息和随机森林的混合变量选择算法[J].吉林大学学报（理学版）,2017,55(4):933-939,7.基金项目
国家自然科学基金(批准号:61573266)、 国家自然科学基金青年基金(批准号:11626034 （批准号:61573266）
61602010)、 陕西省教育厅科研计划项目(批准号:16JK1047)和西安电子科技大学博士科研启动项目(批准号:ZK2017020). （批准号:16JK1047）
