GAN based Data Augmentation to Resolve Class Imbalance

TL;DR

This paper proposes using GANs to generate synthetic minority class data to address class imbalance in credit card fraud detection, improving machine learning model performance.

Contribution

It introduces a GAN-based data augmentation method specifically designed to balance class distribution in fraud detection datasets.

Findings

GAN-generated data improves classifier accuracy

Synthetic minority data enhances model generalization

Method effectively reduces class imbalance issues

Abstract

The number of credit card fraud has been growing as technology grows and people can take advantage of it. Therefore, it is very important to implement a robust and effective method to detect such frauds. The machine learning algorithms are appropriate for these tasks since they try to maximize the accuracy of predictions and hence can be relied upon. However, there is an impending flaw where in machine learning models may not perform well due to the presence of an imbalance across classes distribution within the sample set. So, in many related tasks, the datasets have a very small number of observed fraud cases (sometimes around 1 percent positive fraud instances found). Therefore, this imbalance presence may impact any learning model's behavior by predicting all labels as the majority class, hence allowing no scope for generalization in the predictions made by the model. We trained Generative Adversarial Network(GAN) to generate a large number of convincing (and reliable) synthetic examples of the minority class that can be used to alleviate the class imbalance within the training set and hence generalize the learning of the data more effectively.