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

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2 Author(s)
Qin Hua ; Sch. of Comput. & Electron. Inf., Guangxi Univ., Nanning, China ; Xu Yan-zi

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.

Published in:

E-Business and E-Government (ICEE), 2010 International Conference on

Date of Conference:

7-9 May 2010