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Improved Support Vector Machine Using Multiple SVM-RFE for Cancer Classification
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@article{IJASEIT3394,
author = {Nurul Nadzirah Mohd Hasri and Nies Hui Wen and Chan Weng Howe and Mohd Saberi Mohamad and Safaai Deris and Shahreen Kasim},
title = {Improved Support Vector Machine Using Multiple SVM-RFE for Cancer Classification},
journal = {International Journal on Advanced Science, Engineering and Information Technology},
volume = {7},
number = {4-2},
year = {2017},
pages = {1589--1594},
keywords = {support vector machine (SVM); multiple support vector machine- recursive feature elimination (MSVM-RFE); leukemia; lung cancer},
abstract = {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.
},
issn = {2088-5334},
publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=3394},
doi = {10.18517/ijaseit.7.4-2.3394}
}
EndNote
%A Mohd Hasri, Nurul Nadzirah %A Wen, Nies Hui %A Howe, Chan Weng %A Mohamad, Mohd Saberi %A Deris, Safaai %A Kasim, Shahreen %D 2017 %T Improved Support Vector Machine Using Multiple SVM-RFE for Cancer Classification %B 2017 %9 support vector machine (SVM); multiple support vector machine- recursive feature elimination (MSVM-RFE); leukemia; lung cancer %! Improved Support Vector Machine Using Multiple SVM-RFE for Cancer Classification %K support vector machine (SVM); multiple support vector machine- recursive feature elimination (MSVM-RFE); leukemia; lung cancer %XSupport 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.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=3394 %R doi:10.18517/ijaseit.7.4-2.3394 %J International Journal on Advanced Science, Engineering and Information Technology %V 7 %N 4-2 %@ 2088-5334
IEEE
Nurul Nadzirah Mohd Hasri,Nies Hui Wen,Chan Weng Howe,Mohd Saberi Mohamad,Safaai Deris and Shahreen Kasim,"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, pp. 1589-1594, 2017. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.7.4-2.3394.
RefMan/ProCite (RIS)
TY - JOUR AU - Mohd Hasri, Nurul Nadzirah AU - Wen, Nies Hui AU - Howe, Chan Weng AU - Mohamad, Mohd Saberi AU - Deris, Safaai AU - Kasim, Shahreen PY - 2017 TI - Improved Support Vector Machine Using Multiple SVM-RFE for Cancer Classification JF - International Journal on Advanced Science, Engineering and Information Technology; Vol. 7 (2017) No. 4-2 Y2 - 2017 SP - 1589 EP - 1594 SN - 2088-5334 PB - INSIGHT - Indonesian Society for Knowledge and Human Development KW - support vector machine (SVM); multiple support vector machine- recursive feature elimination (MSVM-RFE); leukemia; lung cancer N2 -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.
UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=3394 DO - 10.18517/ijaseit.7.4-2.3394
RefWorks
RT Journal Article ID 3394 A1 Mohd Hasri, Nurul Nadzirah A1 Wen, Nies Hui A1 Howe, Chan Weng A1 Mohamad, Mohd Saberi A1 Deris, Safaai A1 Kasim, Shahreen T1 Improved Support Vector Machine Using Multiple SVM-RFE for Cancer Classification JF International Journal on Advanced Science, Engineering and Information Technology VO 7 IS 4-2 YR 2017 SP 1589 OP 1594 SN 2088-5334 PB INSIGHT - Indonesian Society for Knowledge and Human Development K1 support vector machine (SVM); multiple support vector machine- recursive feature elimination (MSVM-RFE); leukemia; lung cancer ABSupport 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.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=3394 DO - 10.18517/ijaseit.7.4-2.3394
