CIGAN: A Python Package for Handling Class Imbalance using Generative Adversarial Networks

TL;DR

CIGAN is a Python package that employs Generative Adversarial Networks to address class imbalance in multi-class classification problems, improving classifier performance on minority classes.

Contribution

This paper introduces the first publicly available tool using GANs for multi-class class imbalance oversampling in machine learning.

Findings

Effective oversampling of minority classes improves classifier accuracy.

Supports arbitrary multi-class classification tasks.

Open-source implementation available on GitHub.

Abstract

A key challenge in Machine Learning is class imbalance, where the sample size of some classes (majority classes) are much higher than that of the other classes (minority classes). If we were to train a classifier directly on imbalanced data, it is more likely for the classifier to predict a new sample as one of the majority classes. In the extreme case, the classifier could completely ignore the minority classes. This could have serious sociological implications in healthcare, as the minority classes are usually the disease classes (e.g., death or positive clinical test result). In this paper, we introduce a software that uses Generative Adversarial Networks to oversample the minority classes so as to improve downstream classification. To the best of our knowledge, this is the first tool that allows multi-class classification (where the target can have an arbitrary number of classes). The code of the tool is publicly available in our github repository (https://github.com/yuxiaohuang/research/tree/master/gwu/working/cigan/code).