MixBoost: Synthetic Oversampling using Boosted Mixup for Handling Extreme Imbalance

Anubha Kabra, Ayush Chopra, Nikaash Puri, Pinkesh Badjatiya, Sukriti Verma, Piyush Gupta, Balaji Krishnamurthy

Abstract: 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 customer churn prediction. We propose a 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 elements of both classes. We evaluate MixBoost on 20 benchmark datasets and show that it outperforms existing approaches. We evaluate the impact of the different components of MixBoost using ablation studies.