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

TL;DR

This paper introduces two novel GAN-based oversampling techniques, SSG and GBO, to improve class imbalance handling in machine learning, outperforming traditional SMOTE on benchmark datasets and producing more Gaussian-like minority samples.

Contribution

The study proposes innovative GAN-based oversampling methods, SSG and GBO, addressing limitations of existing techniques like SMOTE and demonstrating improved performance on benchmark datasets.

Findings

SSG and GBO outperform SMOTE on benchmark datasets

Minor samples from SSG exhibit Gaussian distribution

GAN-based methods enhance class imbalance handling

Abstract

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.