# Balanced Split: A new train-test data splitting strategy for imbalanced datasets

arXiv: https://arxiv.org/abs/2212.11116 · HTML: https://mlsanity.com/paper/2212.11116/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 31 references that are in the map.
- First posted on arXiv 17 Dec 2022.

## Builds on (most cited first, 4 of 4)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning](https://mlsanity.com/paper/1711.05225/) (2017)
- [Quantum generative adversarial networks](https://mlsanity.com/paper/1804.08641/) (2018)
- [SMOTified-GAN for class imbalanced pattern classification problems](https://mlsanity.com/paper/2108.03235/) (2021)

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

- [Revolutionizing Communication with Deep Learning and XAI for Enhanced Arabic Sign Language Recognition](https://mlsanity.com/paper/2501.08169/) (2025)

## Abstract (arXiv)

Classification data sets with skewed class proportions are called imbalanced. Class imbalance is a problem since most machine learning classification algorithms are built with an assumption of equal representation of all classes in the training dataset. Therefore to counter the class imbalance problem, many algorithm-level and data-level approaches have been developed. These mainly include ensemble learning and data augmentation techniques. This paper shows a new way to counter the class imbalance problem through a new data-splitting strategy called balanced split. Data splitting can play an important role in correctly classifying imbalanced datasets. We show that the commonly used data-splitting strategies have some disadvantages, and our proposed balanced split has solved those problems.