Deep Learning for Imbalance Data Classification using Class Expert Generative Adversarial Network

TL;DR

This paper introduces CE-GAN, a novel deep learning architecture that effectively handles imbalanced data classification without assuming data imbalance, by capturing detailed class characteristics.

Contribution

The paper proposes CE-GAN, a new deep learning model that improves imbalance data classification by not assuming data imbalance and capturing class-specific details.

Findings

CE-GAN outperforms traditional methods on imbalance datasets.

CE-GAN accurately captures class-specific features.

The approach does not require data balancing techniques.

Abstract

Without any specific way for imbalance data classification, artificial intelligence algorithm cannot recognize data from minority classes easily. In general, modifying the existing algorithm by assuming that the training data is imbalanced, is the only way to handle imbalance data. However, for a normal data handling, this way mostly produces a deficient result. In this research, we propose a class expert generative adversarial network (CE-GAN) as the solution for imbalance data classification. CE-GAN is a modification in deep learning algorithm architecture that does not have an assumption that the training data is imbalance data. Moreover, CE-GAN is designed to identify more detail about the character of each class before classification step. CE-GAN has been proved in this research to give a good performance for imbalance data classification.