Bagging Based Ensemble Classification Method on Imbalance Datasets

Abstract

In the last few years, the problem of class imbalances is a challenging problem in data mining community. The class imbalance occurs when one of the classes in the data has a larger number than others. That condition causing the classification being not optimum because the larger class gave more influences in the classification. Some cases of class imbalance issues become a very important thing, for example, to detect cheating in banking operations, network trouble, cancer diagnose, and prediction of technical failure. This study conducts a bagging based ensemble method to overcome the problem of class imbalance on 14 datasets. The purpose of this research is to see the ability of some bagging based ensemble methods on overcoming the class imbalance problem. The results obtained by using OverBagging method are more stable than other bagging based methods in various datasets.