Hybrid classification model of correlation-based feature selection and support vector machine

Abstract

In this paper, we propose a hybrid classification model, which has correlation based filter feature selection algorithm and support vector machine as a classifier. In this method, features are ordered according to their Absolute correlation value with respect to the class attribute. Then top K Features are selected from ordered list of features to form a reduced dataset. The classification accuracy is measured using SVM classifiers with and without extending features of the reduced dataset. This proposed classifier model is applied to five high-dimensional binary class datasets. It is observed that the proposed method yields higher classification accuracies in the case of three out of five high dimensional datasets with a reasonably small number of features.