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A novel ensemble classifier by combining sampling and genetic algorithm to combat multiclass imbalanced problems

To handle datasets with imbalanced classes is an exigent problem in the area of machine learning and data mining. Though a lot of work has been done by many researchers in the literature for two-class imbalanced problems, the multiclass problems still need to be explored. In this paper, we propose sampling and genetic algorithm based ensemble classifier (SA-GABEC) to handle imbalanced classes. SA-GABEC tries to find the best subset of classifiers for a given sample that is precise in predictions and can create an acceptable diversity in features subspace. These subsets of classifiers are fused together to give better predictions as compared to a single classifier. Moreover, this paper also proposes modified SA-GABEC which performs the feature selection before applying sampling and outperforms SA-GABEC. The performance of the proposed classifiers is evaluated and compared with GAB-EPA, Adaboost and bagging using minority class recall and extended G-mean.