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

arXiv: https://arxiv.org/abs/1807.04585 · HTML: https://mlsanity.com/paper/1807.04585/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2018.

## Built on by (most cited first, 4 of 4)

- [RA-GCN: Graph Convolutional Network for Disease Prediction Problems with Imbalanced Data](https://mlsanity.com/paper/2103.00221/) (2021)
- [A Distributed Approach to Meteorological Predictions: Addressing Data Imbalance in Precipitation Prediction Models through Federated Learning and GANs](https://mlsanity.com/paper/2310.13161/) (2023)
- [Generating Reliable Process Event Streams and Time Series Data based on Neural Networks](https://mlsanity.com/paper/2103.05462/) (2021)
- [Towards to Robust and Generalized Medical Image Segmentation Framework](https://mlsanity.com/paper/2108.03823/) (2021)

## Abstract (arXiv)

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.