A novel approach to pre-extracting support vectors based on the theory of belief functions

Applications of the support vector machine (SVM) in the large scale datasets are seriously hampered by its high computational cost for training. In SVM training, the classification hyperplane is determined by support vectors (SVs). If those samples likely to be SVs can be pre-extracted and used for training, the computational cost can be reduced without the loss of classification accuracy. An approach to pre-extracting SVs is proposed where the training samples' uncertainty in terms of classification is modeled using belief functions. Those samples with a higher degree of uncertainty are more likely to be SVs. Our approach can also detect outliers and noisy samples. Experimental results based on benchmark datasets show that the proposed approach performs better compared with traditional approaches, where the training time is significantly reduced (approximate to one or two orders of magnitude), meanwhile it can obtain good classification accuracies.