SMOTEMultiBoost: Leveraging the SMOTE with MultiBoost to Confront the Class Imbalance in Supervised Learning

Abstract

Class imbalance problem is being manifoldly confronted by researchers due to the increasing amount of complicated data. Common classification algorithms are impoverished to perform effectively on imbalanced datasets. Larger class cases typically outbalance smaller class cases in class imbalance learning. Common classification algorithms raise larger class performance owing to class imbalance in data and overall improvement in accuracy as their goal while lowering performance on smaller class. Furthermore, these algorithms deal false positive and false negative in an even way and regard equal cost of misclassifying cases. Meanwhile, different ensemble solutions have been proposed over the years for class imbalance learning but these approaches hamper the performance of larger class as emphasizing on the small class cases. The intuition of this overall degraded outcome would be the low diversity in ensemble solutions and overfitting or underfitting in data resampling techniques. To overcome these problems, we suggest a hybrid ensemble method by leveraging MultiBoost ensemble and Synthetic Minority Over-sampling TEchnique (SMOTE). Our suggested solution leverage the effectiveness of its elements. Therefore, it improves the outcome of the smaller class by reinforcing its space and limiting error in prediction. The proposed method shows improved performance as compare to numerous other algorithms and techniques in experiments.