# Survey of Imbalanced Data Methodologies

arXiv: https://arxiv.org/abs/2104.02240 · HTML: https://mlsanity.com/paper/2104.02240/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 24 references that are in the map.
- First posted on arXiv 6 Apr 2021.

## Built on by (most cited first, 2 of 2)

- [Absolute Evaluation Measures for Machine Learning: A Survey](https://mlsanity.com/paper/2507.03392/) (2025)
- [Using Neural Networks by Modelling Semi-Active Shock Absorber](https://mlsanity.com/paper/2207.09141/) (2022)

## Abstract (arXiv)

Imbalanced data set is a problem often found and well-studied in financial industry. In this paper, we reviewed and compared some popular methodologies handling data imbalance. We then applied the under-sampling/over-sampling methodologies to several modeling algorithms on UCI and Keel data sets. The performance was analyzed for class-imbalance methods, modeling algorithms and grid search criteria comparison.