Feature selection for support vector regression using a genetic algorithm

Shannon McKearnan, David M. Vock, G. Elisabeta Marai, Guadalupe Canahuate, Clifton D. Fuller, Julian Wolfson

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Support vector regression (SVR) is particularly beneficial when the outcome and predictors are nonlinearly related. However, when many covariates are available, the method's flexibility can lead to overfitting and an overall loss in predictive accuracy. To overcome this drawback, we develop a feature selection method for SVR based on a genetic algorithm that iteratively searches across potential subsets of covariates to find those that yield the best performance according to a user-defined fitness function. We evaluate the performance of our feature selection method for SVR, comparing it to alternate methods including LASSO and random forest, in a simulation study. We find that our method yields higher predictive accuracy than SVR without feature selection. Our method outperforms LASSO when the relationship between covariates and outcome is nonlinear. Random forest performs equivalently to our method in some scenarios, but more poorly when covariates are correlated. We apply our method to predict donor kidney function 1 year after transplant using data from the United Network for Organ Sharing national registry.

Original languageEnglish (US)
Pages (from-to)295-308
Number of pages14
JournalBiostatistics
Volume24
Issue number2
DOIs
StatePublished - Apr 1 2023

Bibliographical note

Publisher Copyright:
© The Author 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: [email protected].

Keywords

  • Genetic algorithm
  • Support vector regression
  • Variable selection

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural

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