# Neural Network Based Undersampling Techniques

arXiv: https://arxiv.org/abs/1908.06487 · HTML: https://mlsanity.com/paper/1908.06487/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2019.

## Built on by (most cited first, 5 of 5)

- [Under-bagging Nearest Neighbors for Imbalanced Classification](https://mlsanity.com/paper/2109.00531/) (2021)
- [An Asymmetric Contrastive Loss for Handling Imbalanced Datasets](https://mlsanity.com/paper/2207.07080/) (2022)
- [Neural Network Architecture for Database Augmentation Using Shared Features](https://mlsanity.com/paper/2302.01374/) (2023)
- [Rare Class Prediction Model for Smart Industry in Semiconductor Manufacturing](https://mlsanity.com/paper/2406.04533/) (2024)
- [Beyond 5G Network Failure Classification for Network Digital Twin Using Graph Neural Network](https://mlsanity.com/paper/2406.06595/) (2024)

## Abstract (arXiv)

Class imbalance problem is commonly faced while developing machine learning models for real-life issues. Due to this problem, the fitted model tends to be biased towards the majority class data, which leads to lower precision, recall, AUC, F1, G-mean score. Several researches have been done to tackle this problem, most of which employed resampling, i.e. oversampling and undersampling techniques to bring the required balance in the data. In this paper, we propose neural network based algorithms for undersampling. Then we resampled several class imbalanced data using our algorithms and also some other popular resampling techniques. Afterwards we classified these undersampled data using some common classifier. We found out that our resampling approaches outperform most other resampling techniques in terms of both AUC, F1 and G-mean score.