A Novel Method for Training Large Scale E-Business SVM Models in a Grid Computing Environment

Abstract

The Support Vector Machines (SVM) become popular E-Business data mining tools recently, and the datasets of E-Business are usually large-scale. If Support Vector Machines are trained on large-scale datasets, the training time will be very long and the classifier’s accuracy will become lower too. As training a large-scale SVM is equated to solve a large-scale quadratic programming (QP) problem, so Path Following Interior Point Method (IPM) that can efficiently solve large scale QP problem in polynomial time is proposed to construct a new SVM learning algorithm on large-scale datasets. To improve the SVM learning efficiency, the dimensions of IPM direction equations are degraded first, then LDLT parallel decomposition method is used to solve the direction sub-equations efficiently, and the parallel algorithm is implemented in the ProActive grid-computing environment. The experiment results show that the new parallel SVM training algorithm is efficient and the SVM classifying accuracy is higher than libsvm.