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

Azal Ahmad Khan, Omkar Chaudhari, Rohitash Chandra

Abstract: Highlights•Class imbalance (CI) in classification problems arises one class is lower than the other classes.•We present a computational review to evaluate data augmentation and ensemble learning methods for CI problems.•We propose a general framework that evaluates 10 data augmentation and 10 ensemble learning methods for CI problems.•Our objective is to identify the most effective combination for improving classification performance on imbalanced datasets.•The results indicate that the combinations can significantly improve classification performance on imbalanced datasets.