# Imbalanced Class Data Performance Evaluation and Improvement using Novel Generative Adversarial Network-based Approach: SSG and GBO

arXiv: https://arxiv.org/abs/2210.12870 · HTML: https://mlsanity.com/paper/2210.12870/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 36 references that are in the map.
- First posted on arXiv 23 Oct 2022.

## Builds on (most cited first, 8 of 8)

- [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017)
- [Rethinking the Value of Labels for Improving Class-Imbalanced Learning](https://mlsanity.com/paper/2006.07529/) (2020)
- [Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification](https://mlsanity.com/paper/1801.02385/) (2018)
- [PacGAN: The power of two samples in generative adversarial networks](https://mlsanity.com/paper/1712.04086/) (2017)
- [Machine learning based disease diagnosis: A comprehensive review](https://mlsanity.com/paper/2112.15538/) (2021)
- [A Novel GAN-based Fault Diagnosis Approach for Imbalanced Industrial Time Series](https://mlsanity.com/paper/1904.00575/) (2019)
- [SMOTified-GAN for class imbalanced pattern classification problems](https://mlsanity.com/paper/2108.03235/) (2021)
- [Class Imbalance Problem in Data Mining Review](https://mlsanity.com/paper/1305.1707/) (2013)

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

- [BSGAN: A Novel Oversampling Technique for Imbalanced Pattern Recognitions](https://mlsanity.com/paper/2305.09777/) (2023)

## Abstract (arXiv)

Class imbalance in a dataset is one of the major challenges that can significantly impact the performance of machine learning models resulting in biased predictions. Numerous techniques have been proposed to address class imbalanced problems, including, but not limited to, Oversampling, Undersampling, and cost-sensitive approaches. Due to its ability to generate synthetic data, oversampling techniques such as the Synthetic Minority Oversampling Technique (SMOTE) is among the most widely used methodology by researchers. However, one of SMOTE's potential disadvantages is that newly created minor samples may overlap with major samples. As an effect, the probability of ML models' biased performance towards major classes increases. Recently, generative adversarial network (GAN) has garnered much attention due to its ability to create almost real samples. However, GAN is hard to train even though it has much potential. This study proposes two novel techniques: GAN-based Oversampling (GBO) and Support Vector Machine-SMOTE-GAN (SSG) to overcome the limitations of the existing oversampling approaches. The preliminary computational result shows that SSG and GBO performed better on the expanded imbalanced eight benchmark datasets than the original SMOTE. The study also revealed that the minor sample generated by SSG demonstrates Gaussian distributions, which is often difficult to achieve using original SMOTE.