Support Vector classification for large data sets by reducing training data with change of classes

In recent years support vector machines (SVM) has received considerable attention due to its high generalization ability and performance for a wide range of applications. However, the most important problem of this method is slow training for classification problems with a large data sets because the quadratic form is completely dense and the memory requirements grow with the square of the number of data points. This paper presents a novel SVM classification approach for large data sets by reducing training data and train the support vector machine using only these data. In this algorithm, a first stage uses SVM classification on a small data set in order to gets a sketch of classes distribution and labels the support vectors as a data set with label +1 and the other points as a data set with label -1. We call this change of classes. Then the algorithm obtains the classification hyperplane and classify the original input data set, the data points obtained with label +1 constitute the data points in the boundary of each original class and represent the most important data points, these data points are used as training data for a posterior SVM classification. The effectiveness of the approach proposed is supported by experimental results.