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 Ensemble with Bagging (bootstrap aggregating). In step one, We explored a novel Genetic Algorithm wrapped Soft Margin Support Vector Machine algorithm to produce the optimal subset of features set. This step greatly improved the quality of classification by eliminating irrelevant features. In Step two, we used the ensemble of SVM with bagging (bootstrap aggregating). We trained every single Support Vector Machine Classifier separately using the randomly chosen samples from training dataset through a bootstrap technique. Then, these individually trained Support Vector Machine Classifiers are aggregated using the double-layer hierarchical combining to produce a joint decision. To evaluate performance and effectiveness, we applied the proposed model on UCI Arrhythmia (standard 12 lead ECG signal recordings) data set to classify arrhythmia into normal and abnormal subjects. With the proposed model, we got a promising true classification accuracy rate. The method is also comparable with state of art classifiers and other methods present in the related literature. The outcomes of the experiment and statistical analysis pointed out that our model is a very useful and practical approach for ECG arrhythmia classification.