Abstract: This paper analyzed the advantages and disadvantages of fuzzy rough set based support vector machines(FRSVMs). FRSVMs arc generated by modifying constraints of hard margin support vector machines(SVMs) to get better generalization ability. Although having considered inconsistency between conditional attributes and decision attributes of training samples in datasets,FRSVMs construct the optimal hyperplane which must classify all the training samples correctly. So FRSVMs arc sensitive to noises. Fuzzy rough set based soft margin support vector machines(GFRSVMs) were proposed in this paper to overcome this shortcomings. C-FRSVMs use Gaussian kernel function as their fuzzy similarity relation, consider inconsistency between conditional attributes and decision labels of the samples in datasets,allow training samples to be misclassified during constructing the optimal hyperplane in the training process,punish the misclassification degrees of training samples in their original optimization problems. C-FRSVMs construct the optimal hyperplane by considering both maximal margin and minimal misclassification errors. So C-FRSVMs are less sensifive to noises than FRSVMs. Experimental results show that the proposed approach can obtain higher test accuracy compared with hard margin SVMs,soft margin support vector machines(C-SVMs) and FRSVMs. So,C-FRSVMs can get better generalization ability compared with FRSVMs.

Key words: Support vector machincs,Rough sct,Fuzzy rough set,Fuzzy membership,FRSVMs