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http://dx.doi.org/10.6109/jicce.2019.17.1.14

Enhancing Gene Expression Classification of Support Vector Machines with Generative Adversarial Networks  

Huynh, Phuoc-Hai (Information Technology Faculty, An Giang University)
Nguyen, Van Hoa (Information Technology Faculty, An Giang University)
Do, Thanh-Nghi (College of Information Technology, Can Tho University)
Publication Information
Journal of information and communication convergence engineering / v.17, no.1, 2019 , pp. 14-20 More about this Journal
Abstract
Currently, microarray gene expression data take advantage of the sufficient classification of cancers, which addresses the problems relating to cancer causes and treatment regimens. However, the sample size of gene expression data is often restricted, because the price of microarray technology on studies in humans is high. We propose enhancing the gene expression classification of support vector machines with generative adversarial networks (GAN-SVMs). A GAN that generates new data from original training datasets was implemented. The GAN was used in conjunction with nonlinear SVMs that efficiently classify gene expression data. Numerical test results on 20 low-sample-size and very high-dimensional microarray gene expression datasets from the Kent Ridge Biomedical and Array Expression repositories indicate that the model is more accurate than state-of-the-art classifying models.
Keywords
Classification; Support vector machines; Generative adversarial networks; Enhancing data; Gene expression data;
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