SVM has been successfully employed to solve the analysis of gene expression data. However

there are still open issues which need to be addressed: ① SVM does not offer the mechanism of automatic internal relevant feature selection; ② There are no simple and effective means to confirm the appropriate parameters setting of SVM. In this study

total marginbased adaptive fuzzy support vector machine (TAFSVM) which has good quality is proposed. In addition

it is proposed an evolutionary approach to design a TAFSVMbased classifier (named ITAFSVM) by simultaneous optimization of automatic feature selection and parameters tuning using an intelligent genetic algorithm (IGA)

combined with 10fold crossvalidation regarded as an estimator of generalization ability. Subsequently

the model of ITAFSVM is used to analyze four gene expression datasets. Comparisons with evolutionary support vector machine and a combination of roughbased feature selection and RBF neural network are reported. The experimental results indicate that the proposed ITAFSVM model can not only accomplish automatic feature selection

but also achieve higher classification accuracy

stable and faster speed.