# Neural Network Based Undersampling Techniques

## TL;DR

This paper introduces neural network-based undersampling algorithms to address class imbalance in machine learning, demonstrating superior performance over existing resampling methods in key evaluation metrics.

## Contribution

The paper proposes novel neural network-based undersampling techniques and empirically shows they outperform traditional resampling methods on various datasets.

## Key findings

- Neural network undersampling improves AUC, F1, and G-mean scores.
- Our methods outperform popular resampling techniques.
- Results are consistent across multiple datasets.

## Abstract

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.