Son Min Jae, Jung Seung Won, Hwang Een Jun

KIPS Transactions on Software and Data Engineering, Vol. 8, No. 7, pp. 311-316, Jul. 2019
https://doi.org/10.3745/KTSDE.2019.8.7.311,   PDF Download:
Keywords: Imbalanced Data, CGAN, Deep Learning, Over-Sampling
Abstract

Classification problem is to predict the class to which an input data belongs. One of the most popular methods to do this is training a machine learning algorithm using the given dataset. In this case, the dataset should have a well-balanced class distribution for the best performance. However, when the dataset has an imbalanced class distribution, its classification performance could be very poor. To overcome this problem, we propose an over-sampling scheme that balances the number of data by using Conditional Generative Adversarial Networks (CGAN). CGAN is a generative model developed from Generative Adversarial Networks (GAN), which can learn data characteristics and generate data that is similar to real data. Therefore, CGAN can generate data of a class which has a small number of data so that the problem induced by imbalanced class distribution can be mitigated, and classification performance can be improved. Experiments using actual collected data show that the over-sampling technique using CGAN is effective and that it is superior to existing over-sampling techniques.

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