Bagging based ensemble of Support Vector Machines with improved elitist GA-SVM features selection for cardiac arrhythmia classification

Abstract

In this study, we proposed a robust model to classify ECG arrhythmia. The proposed approach has two stages: (1) generation of an optimal feature subset using GA and (2) the classification and evaluation of the reduced ECG arrhythmia data using SVM En