Gene expression data analysis using support vector machines

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Abstract

Cancer classification is an important problem both for clinical treatment and for biomedical research. Considering the good performance of support vector machines (SVMs) on solving pattern recognition problems, we use a C-SVM to process the B-cell lymphoma data. The principal components analysis (PCA) is used for gene selection. A voting scheme is used to do multi-group classification by k(k-1) binary SVMs. The classification results show that SVMs are effective tools for this problem.

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