Abstract A fuzzy support vector machine (SVM) based on border vector extraction is presented. It overcomes the disadvantage of the sensitivity to noises and the outliers in the training samples. Border vectors, which are possible support vectors, are selected as new samples to train SVMs. The number of training samples is reduced and thus the training speed is improved. The fuzzy membership is defined according to the distance from border vectors and outliers to their hypersphere centers. Consequently the effect of noises and outliers is weakened and support vectors are improved to design a classifier. Experimental results show that by the proposed method the machine is less sensitive to noises and outliers than by the traditional SVMs and the fuzzy SVMs based on the distance between a sample and its cluster center. Furthermore, the proposed method has better generalization ability and higher learning speed than the others.
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2008, Vol. 21 
