Feature Selection for Support Vector Regression Using a Genetic Algorithm

Authors

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

Publication Date

4-14-2023

Journal

Biostatistics

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.

Keywords

Humans, Algorithms, Regression Analysis, Support Vector Machine

DOI

10.1093/biostatistics/kxab022

PMID

34494086

PMCID

PMC10102886

PubMedCentral® Posted Date

9-8-2021

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes