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

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

INTERNATIONAL JOURNAL OF ADVANCED AND APPLIED SCIENCES, cilt.3, sa.12, ss.26-31, 2016 (ESCI) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 3 Sayı: 12
  • Basım Tarihi: 2016
  • Doi Numarası: 10.21833/ijaas.2016.12.004
  • Dergi Adı: INTERNATIONAL JOURNAL OF ADVANCED AND APPLIED SCIENCES
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI)
  • Sayfa Sayıları: ss.26-31
  • Anahtar Kelimeler: Variable selection, Multicollinearity, Genetic algorithm, Multivariate adaptive regression splines
  • Anadolu Üniversitesi Adresli: Evet

Özet

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/).