Variable selection with genetic algorithm and multivariate adaptive regression splines in the presence of multicollinearity

Creative Commons License

Kilinc B. K., AŞIKGİL B., Erar A., YAZICI B.

INTERNATIONAL JOURNAL OF ADVANCED AND APPLIED SCIENCES, vol.3, no.12, pp.26-31, 2016 (ESCI) identifier

  • Publication Type: Article / Article
  • Volume: 3 Issue: 12
  • Publication Date: 2016
  • Doi Number: 10.21833/ijaas.2016.12.004
  • Journal Name: INTERNATIONAL JOURNAL OF ADVANCED AND APPLIED SCIENCES
  • Journal Indexes: Emerging Sources Citation Index (ESCI)
  • Page Numbers: pp.26-31
  • Keywords: Variable selection, Multicollinearity, Genetic algorithm, Multivariate adaptive regression splines
  • Anadolu University Affiliated: Yes

Abstract

In this paper, it is aimed to determine the true regressors explaining the dependent variable in multiple linear regression models and also to find the best model by using two different approaches in the presence of low, medium and high multicollinearity. These approaches compared in this study are genetic algorithm and multivariate adaptive regression splines. A comprehensive Monte Carlo experiment is performed in order to examine the performance of these approaches. This study exposes that nonparametric methods can be preferred for variable selection in order to obtain the best model when there is a multicollinearity problem in the small, medium or large data sets. (C) 2016 The Authors. Published by IASE. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).