# MixBoost: Synthetic Oversampling with Boosted Mixup for Handling Extreme Imbalance

arXiv: https://arxiv.org/abs/2009.01571 · HTML: https://mlsanity.com/paper/2009.01571/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2020.

## Built on by (most cited first, 6 of 6)

- [Balanced-MixUp for Highly Imbalanced Medical Image Classification](https://mlsanity.com/paper/2109.09850/) (2021)
- [Interdisciplinary Fairness in Imbalanced Research Proposal Topic Inference: A Hierarchical Transformer-based Method with Selective Interpolation](https://mlsanity.com/paper/2309.01717/) (2023)
- [SMSMix: Sense-Maintained Sentence Mixup for Word Sense Disambiguation](https://mlsanity.com/paper/2212.07072/) (2022)
- [Hierarchical MixUp Multi-label Classification with Imbalanced Interdisciplinary Research Proposals](https://mlsanity.com/paper/2209.13912/) (2022)
- [Tackling Diverse Minorities in Imbalanced Classification](https://mlsanity.com/paper/2308.14838/) (2023)
- [Mix from Failure: Confusion-Pairing Mixup for Long-Tailed Recognition](https://mlsanity.com/paper/2411.07621/) (2024)

## Abstract (arXiv)

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.