ABSTRACT
Learning with imbalanced datasets is one of the key topics in the machine learning community. Bagging ensemble is an efficient algorithm for imbalanced datasets. This research work proposes new ensemble classification methods with homogeneous ensemble classifiers using bagging, 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 using a standard automobile dataset. The are three main parts to the proposed approach: the pre-processing phase, the classification phase and the combining phase. A wide range of comparative experiments are carried out to assess the performance of the proposed homogeneous ensemble classifiers compared with other standard homogeneous ensemble methods, which include error-correcting output codes, Dagging. The proposed ensemble methods provide significantly greater accuracy than individual classifiers and the proposed bagged RBF and SVM performs significantly better than ECOC and Dagging.
