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http://dx.doi.org/10.3745/KTSDE.2019.8.7.311

A Deep Learning Based Over-Sampling Scheme for Imbalanced Data Classification  

Son, Min Jae (고려대학교 전기전자공학과)
Jung, Seung Won (고려대학교 전기전자공학과)
Hwang, Een Jun (고려대학교 전기전자공학과)
Publication Information
KIPS Transactions on Software and Data Engineering / v.8, no.7, 2019 , pp. 311-316 More about this Journal
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.
Keywords
Imbalanced Data; CGAN; Deep Learning; Over-Sampling;
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