Evaluation of Ensemble Methods for Imbalanced Classification Problem

Abstract

In this research work, new ensemble classification methods are proposed with homogeneous ensemble classifier using bagging and heterogeneous ensemble classifier using arcing and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using Radial Basis Function (RBF) and Support Vector Machine (SVM) as base classifiers. The feasibility and the benefits of the proposed approaches are demonstrated by the means of standard dataset of automobile. The main originality of the proposed approach is based on three main parts: pre-processing phase, classification phase and combining phase. A wide range of comparative experiments are conducted for standard dataset of automobile. The proposed ensemble methods provide significant improvement of accuracy compared to individual classifiers. Also heterogeneous models exhibit better results than homogeneous models for standard dataset of automobile.