Cite Article

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

Choose citation format

BibTeX

@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
%X 

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.

%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
AB 

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.

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