Citation

Tan, Phit Ling and Tan, Shing Chiang and Lim, Chee Peng and Khor, Swee Eng (2011) A modified two-stage SVM-RFE model for cancer classification using microarray data. In: Neural Information Processing. Lecture Notes in Computer Science (7062). Springer Berlin Heidelberg, pp. 668-675. ISBN 978-3-642-24954-9

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Abstract

Gene selection is one of the research issues for improving classification of microarray gene expression data. In this paper, a gene selection algorithm, which is based on the modified Recursive Feature Elimination (RFE) method, is integrated with a Support Vector Machine (SVM) to build a hybrid SVM-RFE model for cancer classification. The proposed model operates with a two-stage gene elimination scheme for finding a subset of expressed genes that indicate a disease. The effectiveness of the proposed model is evaluated using a multi-class lung cancer problem. The results show that the proposed SVM-RFE model is able to perform well with high classification accuracy rates.

Item Type:	Book Section
Additional Information:	18th International Conference, ICONIP 2011, Shanghai, China, November 13-17, 2011, Proceedings, Part I
Subjects:	T Technology > T Technology (General)
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	15 Jan 2014 02:37
Last Modified:	15 Jan 2014 02:37
URII:	http://shdl.mmu.edu.my/id/eprint/4841

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