Feature selection for support vector regression using a genetic algorithm

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

doi:10.1093/biostatistics/kxab022

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Feature selection for support vector regression using a genetic algorithm

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Feature selection for support vector regression using a genetic algorithm

Shannon B Mckearnan, David M Vock, G Elisabeta Marai, Guadalupe Canahuate, Clifton D Fuller and Julian Wolfson

Biostatistics, Vol.24(2), pp.295-308

04/2023

DOI: 10.1093/biostatistics/kxab022

PMCID: PMC10102886

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https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10102886View

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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.

Details

Title: Subtitle: Feature selection for support vector regression using a genetic algorithm
Creators: Shannon B Mckearnan - University of Minnesota
David M Vock - University of Minnesota
G Elisabeta Marai - University of Illinois at Chicago
Guadalupe Canahuate - University of Iowa
Clifton D Fuller - The University of Texas MD Anderson Cancer Center
Julian Wolfson - University of Minnesota
Resource Type: Journal article
Publication Details: Biostatistics, Vol.24(2), pp.295-308
DOI: 10.1093/biostatistics/kxab022
PMCID: PMC10102886
ISSN: 1468-4357
Grant note: DOI: 10.13039/100000054, name: NCI, award: R01CA214825, R01CA225190
Language: English
Electronic publication date: 09/08/2021
Date published: 04/2023
Academic Unit: Electrical and Computer Engineering
Record Identifier: 9984231912402771

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