Data may be afflicted with uncertainty. Uncertain data may be shown by an interval value or in general by a fuzzy set. A number of classification methods have considered uncertainty in features of samples. Some of these classification methods are extended version of the support vector machines (SVMs), such as the Interval-SVM (ISVM), Holder-ISVM and Distance-ISVM, which are used to obtain a classifier for separating samples whose features are interval values. In this paper, we extend the SVM for robust classification of linear/non-linear separable data whose features are fuzzy numbers. The support of such training data is shown by a hypercube. Our proposed method tries to obtain a hyperplane (in the input space or in a high-dimensional feature space) such that the nearest point of the hypercube of each training sample to the hyperplane is separated with the widest symmetric margin. This strategy can reduce the misclassification probability of our proposed method. Our experimental results on six real data sets show that the classification rate of our novel method is better than or equal to the classification rate of the well-known SVM, ISVM, Holder-ISVM and Distance-ISVM for all of these data sets.