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

• Published in: Journal of Information and Communication Convergence Engineering, 17(1) Vol. 17; no. 1; pp. 14 - 20
• Main Authors: Phuoc-Hai Huynh, Van Hoa Nguyen, Thanh-Nghi Do
• Format: Journal Article
• Language: English
• Published: 한국정보통신학회JICCE 2019; 한국정보통신학회
• Subjects: 전자/정보통신공학; Support vector machines; Generative adversarial networks; Gene expression data; Enhancing data; Classification
• ISSN: 2234-8255; 2234-8883
• DOI: 10.6109/jicce.2019.17.1.14

Currently, microarray gene expression data take advantage of the sufficient classification of cancers, which addresses theproblems 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 expressionclassification of support vector machines with generative adversarial networks (GAN-SVMs). A GAN that generates new datafrom original training datasets was implemented. The GAN was used in conjunction with nonlinear SVMs that efficientlyclassify gene expression data. Numerical test results on 20 low-sample-size and very high-dimensional microarray geneexpression datasets from the Kent Ridge Biomedical and Array Expression repositories indicate that the model is more accuratethan state-of-the-art classifying models. KCI Citation Count: 0