Hybrid approach redefinition with progressive boosting for class imbalance problem

Abstract

Problems of Class Imbalance in data classification have received attention from many researchers. It is because the imbalance class will affect the accuracy of the classification results. The problem of the imbalance class itself will ignore the minority class, which is a class with a smaller number of instances even though the minority class is an exciting class to observe. In overcoming the imbalanced class problem, it is necessary to pay attention to diversity data, the number of classifiers, and also classification performance. Several methods have been proposed to overcome the imbalanced class problem, one of which is the Hybrid Approach Redefinition Method. This method is a good hybrid ensemble method in dealing with imbalance class problems, which can provide useful diversity data and also a smaller number of classifiers. This research will combine the Hybrid Approach Redefinition by replacing the use of SMOTE Boost by using Progressive Boosting to get better data diversity, a small number of classifiers, and better performance. This study will conduct testing in handling imbalance class problems using datasets sourced from the KEEL-Dataset Repository. The results of this study indicate that the Hybrid Approach Redefinition with Progressive Boosting will provide better results in the number of classifiers, data diversity, and classification performance.