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

  • Received : 2018.11.06
  • Accepted : 2019.02.26
  • Published : 2019.03.31


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.


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Fig. 1. SVM for binary classification.

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Fig. 2. Architecture of a generative adversarial network.

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Fig. 3. Accuracy of these models on 20 datasets.

Table 1. Description characterizes of 20 datasets

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Table 2. Hyper-parameters of GAN-SVM

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Table 3. Classification results on 20 datasets

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Table 4. Accuracy comparison between these models

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