DOI : https://doi.org/10.18517/ijaseit.7.4-2.3394

Improved Support Vector Machine Using Multiple SVM-RFE for Cancer Classification

Nurul Nadzirah Mohd Hasri (1), Nies Hui Wen (2), Chan Weng Howe (3), Mohd Saberi Mohamad (4), Safaai Deris (5), Shahreen Kasim (6)
(1) Artificial Intelligence and Bioinformatics Research Group, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia
(2) Artificial Intelligence and Bioinformatics Research Group, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia
(3) Artificial Intelligence and Bioinformatics Research Group, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia
(4) Faculty of Creative Technology and Heritage, Universiti Malaysia Kelantan, Karung Berkunci 01, 16300, Bachok, Kelantan, Malaysia.
(5) Faculty of Creative Technology and Heritage, Universiti Malaysia Kelantan, Karung Berkunci 01, 16300, Bachok, Kelantan, Malaysia.
(6) Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400, Batu Pahat, Johor, Malaysia.
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How to cite (IJASEIT) :
Mohd Hasri, Nurul Nadzirah, et al. “Improved Support Vector Machine Using Multiple SVM-RFE for Cancer Classification”. International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 4-2, Sept. 2017, pp. 1589-94, doi:10.18517/ijaseit.7.4-2.3394.
Citation Format :
Support Vector Machine (SVM) is a machine learning method and widely used in the area of cancer studies especially in microarray data. Common problem related to the microarray data is that the size of genes is essentially larger than the number of sample. Although SVM is capable in handling large number of genes, better accuracy of classification can be obtained using small number of gene subset. This research proposed Multiple Support Vector Machine- Recursive Feature Elimination (MSVM-RFE) as a gene selection to identify the small number of informative genes. This method is implemented in order to improve the performance of SVM during classification. The effectiveness of the proposed method has been tested on two different datasets of gene expression which are leukemia and lung cancer. In order to see the effectiveness of the proposed method, some methods such as Random Forest and C4.5 Decision Tree are compared in this paper. The result shows that this MSVM-RFE is effective in reducing the number of genes in both datasets thus providing a better accuracy for SVM in cancer classification.
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