A NEW APPROACH TO SELECT THE BEST SUBSET OF PREDICTORS IN LINEAR REGRESSION MODELLING: BI-OBJECTIVE MIXED INTEGER LINEAR PROGRAMMING

作     者：Charkhgard, Hadi Eshragh, Ali 

作者机构：Univ S Florida Dept Ind & Management Syst Engn Tampa FL 33620 USA Univ Newcastle Sch Math & Phys Sci Callaghan NSW 2308 Australia 

出 版 物：《ANZIAM JOURNAL》 (澳大利亚和新西兰工业与应用数学杂志)

年 卷 期：2019年第61卷第1期

页      面：64-75页

核心收录：

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

主　　题：linear regression best subset selection bi-objective mixed integer linear programming 

摘      要：We study the problem of choosing the best subset of p features in linear regression, given n observations. This problem naturally contains two objective functions including minimizing the amount of bias and minimizing the number of predictors. The existing approaches transform the problem into a single-objective optimization problem. We explain the main weaknesses of existing approaches and, to overcome their drawbacks, we propose a bi-objective mixed integer linear programming approach. A computational study shows the efficacy of the proposed approach.