  Training a classification model on a dataset where the instances of one class
outnumber those of the other class is a challenging problem. Such imbalanced
datasets are standard in real-world situations such as fraud detection, medical
diagnosis, and computational advertising. We propose an iterative data
augmentation method, MixBoost, which intelligently selects (Boost) and then
combines (Mix) instances from the majority and minority classes to generate
synthetic hybrid instances that have characteristics of both classes. We
evaluate MixBoost on 20 benchmark datasets, show that it outperforms existing
approaches, and test its efficacy through significance testing. We also present
ablation studies to analyze the impact of the different components of MixBoost.
