Feature selection for support vector regression using a genetic algorithm.
Biostatistics
; 24(2): 295-308, 2023 04 14.
Article
en En
| MEDLINE
| ID: mdl-34494086
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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Análisis de Regresión
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Biostatistics
Año:
2023
Tipo del documento:
Article
País de afiliación:
Estados Unidos
Pais de publicación:
Reino Unido