Journal
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
Volume 15, Issue 6, Pages 475-484Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0129065705000396
Keywords
cancer classification; gene expression data; microarray; support vector machine
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Microarray gene expression data usually have a large number of dimensions, e.g., over ten thousand genes, and a small number of samples, e.g., a few tens of patients. In this paper, we use the support vector machine (SVM) for cancer classification with microarray data. Dimensionality reduction methods, such as principal components analysis (PCA), class-separability measure, Fisher ratio, and t-test, are used for gene selection. A voting scheme is then employed to do multi-group classification by k(k - 1) binary SVMs. We are able to obtain the same classification accuracy but with much fewer features compared to other published results.
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