Exploring effect of preprocessing on classifier ensembles in imbalanced dataset classification

During the last few years, imbalanced data classification issue has gained a great deal of attention. Many real life applications suffer from imbalanced distribution of data that can be handled by using different approaches such as data level, algorithm level or classifier ensembles. Single level as well as multi level classifier ensemble technique has shown improvement in classification performance. Also data level approaches are independent of classifier being used. In past few years, combination of data level and classifier ensemble technique has been applied and has proved to be effective. This paper explores the impact of pre-processing algorithm on the performance of classifier ensemble approach for imbalanced data set. The aim of this study is to investigate the effect of pre-processing on two level classifier ensemble approaches. Experimental work and analysis of results shows that use of pre-processing is not beneficial for Random Subspace Method since results reflect performance degradation while AdaBoost has shown improvement due to application of pre-processing.