A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation

Class imbalance (CI) in classification problems arises when the number of observations belonging to one class is lower than the other classes. Ensemble learning that combines multiple models to obtain a robust model has been prominently used with data augmentation methods to address class imbalance problems. In the last decade, a number of strategies have been added to enhance ensemble learning and data augmentation methods, along with new methods such as generative adversarial networks (GANs). A combination of these has been applied in many studies, but the true rank of different combinations would require a computational review. In this paper, we present a computational review to evaluate data augmentation and ensemble learning methods used to address prominent benchmark CI problems. We propose a general framework that evaluates 10 data augmentation and 10 ensemble learning methods for CI problems. Our objective was to identify the most effective combination for improving classification performance on imbalanced datasets. The results indicate that combinations of data augmentation methods with ensemble learning can significantly improve classification performance on imbalanced datasets. These findings have important implications for the development of more effective approaches for handling imbalanced datasets in machine learning applications.