Ensemble Variable Selection Using Genetic Algorithm

• 발행기관: 한국자료분석학회
• 저자명: Seogyoung Lee Martin Seunghwan Yang Jongkyeong Kang SeungJun Shin
• 간행물 정보: 『한국자료분석학회 학술대회자료집』2022년 하계학술대회 발표집, 111~114쪽, 전체 4쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2022.07.07

영문 초록

Variable selection is one of the most crucial tasks in supervised learning, such as regression and classification. The best subset selection is straightforward and optimal but not practically applicable unless the number of predictors is small. In this article, we propose directly solving the best subset selection via the genetic algorithm(GA), a popular stochastic optimization algorithm based on the principle of Darwinian evolution. To further improve the variable selection performance, we propose to run multiple GA to solve the best subset selection and then synthesize the results, which we call ensemble GA(EGA). The EGA significantly improves variable selection performance. In addition, the proposed method is essentially the best subset selection and hence applicable to a variety of models with different selection criteria. We compare the proposed EGA to existing variable selection methods under various models, including linear regression, Poisson regression, and Cox regression for survival data. Both simulation and real data analysis demonstrate the promising performance of the proposed method.

목차

1. Introduction

키워드

Genetic Algorithm Ensemble Learning Generalized Linear Model Cox Regression