MixBoost: Synthetic Oversampling with Boosted Mixup for Handling Extreme Imbalance

TL;DR

MixBoost is a novel data augmentation technique that intelligently combines instances from different classes to improve classification performance on highly imbalanced datasets, outperforming existing methods.

Contribution

The paper introduces MixBoost, a new iterative oversampling method that combines boosting and mixup strategies for handling extreme class imbalance.

Findings

Outperforms existing imbalance handling methods on 20 benchmarks.

Significant improvements in classification accuracy demonstrated.

Ablation studies confirm the effectiveness of MixBoost components.

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 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.