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. © 2011 Springer-Verlag.
CITATION STYLE
Tan, P. L., Tan, S. C., Lim, C. P., & Khor, S. E. (2011). A modified two-stage SVM-RFE model for cancer classification using microarray data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7062 LNCS, pp. 668–675). https://doi.org/10.1007/978-3-642-24955-6_79
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