This paper proposes a new gene selection algorithm based on support vectors and penalty strategy (SVPS). In detail, cross-validation procedures are performed on training datasets, and in each validation sub-procedure, support vector machines (SVMs) are trained and tested. For each SVM, the support vectors are weighted and combined to form the initial correlation degrees of genes with the class distinction. A penalty strategy is then used to penalize these initial degrees, and the penalized degrees are finally used to produce a criterion for gene selection. The application to the leukaemia dataset shows that the proposed algorithm can identify the key genes related to the class distinction and is competitive to the existing methods.
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