# Statistical Undersampling with Mutual Information and Support Points

arXiv: https://arxiv.org/abs/2412.14527 · HTML: https://mlsanity.com/paper/2412.14527/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 6 references that are in the map.
- First posted on arXiv 19 Dec 2024.

## Built on by (most cited first, 1 of 1)

- [Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information](https://mlsanity.com/paper/2508.07713/) (2025)

## Abstract (arXiv)

Class imbalance and distributional differences in large datasets present significant challenges for classification tasks machine learning, often leading to biased models and poor predictive performance for minority classes. This work introduces two novel undersampling approaches: mutual information-based stratified simple random sampling and support points optimization. These methods prioritize representative data selection, effectively minimizing information loss. Empirical results across multiple classification tasks demonstrate that our methods outperform traditional undersampling techniques, achieving higher balanced classification accuracy. These findings highlight the potential of combining statistical concepts with machine learning to address class imbalance in practical applications.