  Class imbalance (CI) in classification problems arises when the number of
observations belonging to one class is lower than the other. Ensemble learning
combines multiple models to obtain a robust model and 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, and the evaluation of different combinations would enable a better
understanding and guidance for different application domains. In this paper, we
present a computational study to evaluate data augmentation and ensemble
learning methods used to address prominent benchmark CI problems. We present a
general framework that evaluates 9 data augmentation and 9 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 combinations of data augmentation methods with
ensemble learning can significantly improve classification performance on
imbalanced datasets. We find that traditional data augmentation methods such as
the synthetic minority oversampling technique (SMOTE) and random oversampling
(ROS) are not only better in performance for selected CI problems, but also
computationally less expensive than GANs. Our study is vital for the
development of novel models for handling imbalanced datasets.
