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Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 875392, 6 pages
http://dx.doi.org/10.1155/2013/875392
Research Article

A Wavelet Kernel-Based Primal Twin Support Vector Machine for Economic Development Prediction

1College of Economics, Lanzhou University of Technology, 287 Langongping Road, Qilihe District, Lanzhou City 730050, China
2Lanzhou University of Finance and Economics, Gansu 730050, China

Received 17 May 2013; Revised 6 July 2013; Accepted 8 July 2013

Academic Editor: Vishal Bhatnagar

Copyright © 2013 Fang Su and HaiYang Shang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Economic development forecasting allows planners to choose the right strategies for the future. This study is to propose economic development prediction method based on the wavelet kernel-based primal twin support vector machine algorithm. As gross domestic product (GDP) is an important indicator to measure economic development, economic development prediction means GDP prediction in this study. The wavelet kernel-based primal twin support vector machine algorithm can solve two smaller sized quadratic programming problems instead of solving a large one as in the traditional support vector machine algorithm. Economic development data of Anhui province from 1992 to 2009 are used to study the prediction performance of the wavelet kernel-based primal twin support vector machine algorithm. The comparison of mean error of economic development prediction between wavelet kernel-based primal twin support vector machine and traditional support vector machine models trained by the training samples with the 3–5 dimensional input vectors, respectively, is given in this paper. The testing results show that the economic development prediction accuracy of the wavelet kernel-based primal twin support vector machine model is better than that of traditional support vector machine.