SVM Classification for Large Data Sets by Considering Models of Classes Distribution

Despite of good theoretic foundations and high classification accuracy of support vector machines (SVM), normal SVM is not suitable for classification of large data sets, because the training complexity of SVM is very high. This paper presents a novel SVM classification approach for large data sets by considering models of classes distribution (MCD). A first stage uses SVM classification in order to gets a sketch of classes distribution. Then the algorithm obtain the support vectors (SVs) most close between each class and construct a ball using minimum enclosing ball from each pair of SVs with different label. The data points included in the balls constitute the MCD, which is the framework in the boundary of each class and represents the most important data points, these data points are used as training data for a posterior SVM classification. Experimental results show that our approach has good classification accuracy while the training is significantly faster than other SVM classifiers.