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The purpose of this research is to develop a research framework to optimize the results of hybrid ensembles in handling class imbalance issues. The imbalance class is a state in which the classification results give the number of instances in a class much larger than the number of instances in the other class. In machine learning, this problem can reduce the prediction accuracy and also reduce the quality of the resulting decisions. One of the most popular methods of dealing with class imbalance is the method of ensemble learning. Hybrid Ensembles is an ensemble learning method approach that combines the use of bagging and boosting. Optimization of Hybrid Ensembles is done with the intent to reduce the number of classifier and also obtain better data diversity. Based on an iterative methodology, we review, analyze, and synthesize the current state of the literature and propose a completely new research framework for optimizing Hybrid Ensembles. In doing so, we propose a new taxonomy in ensemble learning that yields a new approach of sampling-based Ensembles and will propose an optimization Hybrid Ensembles using Hybrid Approach Redefinition (HAR) Model that combines the use of Hybrid Ensembles and Sampling Based Ensembles methods. We further provide an empirical analysis of the reviewed literature and emphasize the benefits that can be achieved by optimizing Hybrid Ensembles.

Hybrid Approach Redefinition (HAR) model for optimizing hybrid ensembles in handling class imbalance: a review and research framework