Bootstrap Feature Selection in Support Vector Machines for Ventricular Fibrillation Detection

Abstract

Support Vector Machines (SVM) for classification are being paid special attention in a number of practical applications. When using nonlinear Mercer kernels, the mapping of the input space to a highdimensional feature space makes the input feature selection a difficult task to be addressed. In this paper, we propose the use of nonparametric bootstrap resampling technique to provide with a statistical, distribution independent, criterion for input space feature selection. The confidence interval of the difference of error probability between the complete input space and a reduced-in-one-variable input space, is estimated via bootstrap resampling. Hence, a backward variable elimination procedure can be stated, by removing one variable at each step according to its associated confidence interval. A practical example application to early stage detection of cardiac Ventricular Fibrillation (VF) is presented. Basing on a previous nonlinear analysis based on temporal and spectral VF parameters, we use the SVM with Gaussian kernel and bootstrap resampling to provide with the minimum input space feature set that still holds the classification performance of the complete data. The use of bootstrap resampling is a powerful input feature selection procedure for SVM classifiers.