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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.
Neural Networks, 2003. Proceedings of the International Joint Conference on (Volume:3 )
Date of Conference: 20-24 July 2003