Abstract
Support Vector Machine (SVM) is a machine learning method based on Structual Risk Minimization (SRM). In traditional SVM, different selections of hyper-parameters have a significant effect on the forecast performance. Differential evolution algorithm (DE) is a rapid evolutionary algorithm based on the real-code, which can avoid the local optimization by the differential mutation operation between individuals. Simplex searching algorithm is a direct searching algorithm which solves nonconstraint nonlinear programming problems. This paper introduces the application of SVM in the spatial interpolation in geosciences field. It proposes a new method of optimizing parameters of SVM based on DE algorithm and simplex algorithm. Firstly, DE algorithm is used to obtain the initial value of simplex algorithm, then the simplex local searching strategy is applied to optimize SVR parameters for the second time. Moreover, the spatial interpolation simulation is conducted on the standard dataset of SIC2004. The case study illustrates that the proposed algorithm has higher forecast accuracy and proves the validity of the method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Mallet, J.L.: Discrete smooth interpolation in geometric modeling. Computer Aided Design 24(4), 78–193 (1992)
Pozdnoukhov, A.: Support Vector Regression For Automated Robust Spatial Mapping Of Natural Radioactivity. Applied GIS 1(2), 1–10 (2005)
Wang, W., Zhang, X., Lu, W.: Determination of the spread parameter in the Gaussian kernel for classification and regression. Neurocomputing 55(6), 643–663 (2003)
Yang, D., Tian, Y.-J.: The new data mining method - support vector machine. Science Press, Beijing (2004)
Kwok, J.T., Tsang, I.W.: Linear dependency between and the input noise in support vector regression. IEEE Trans. on Neural Networks 14(3), 544–553 (2003)
Yan, G., Li, C., Ma, G.: SVM parameter selection based on hybrid genetic algorithm. Harbin University 40(5), 688–691 (2008)
Wang, K., Yang, S., Dai, T.: A approach optimize support vector machine parameters using genetic algorithm. Computer Applications and Software 26(7), 109–111 (2009)
Wang, P., Qian, L., Cao, J.: Support vector machine parameter optimization based on ant colony algorithm. Journal of Nanjing University: Natural Science 33(4), 464–468 (2009)
Guo, L., Zhao, C.: Parameter optimization algorithm for support vector machine using 10 fold cross validation 45(8), 55–57 (2009)
Mao, R., Wang, X., Xue, X.: An adaptive differential evolution algorithm. Computer Applications and Software 25(12), 7–8 (2009)
Chen, W., Gong, W., Cai, Z.: Orthogonal differential evolution algorithm for optimization in engineering design. Computer Engineering and Applications 44(18), 230–232 (2008)
King S.: Threats Solutions to Web Services Security. Network Security 2003(9) (2003)
Konstantin Beznosov, D.J., Kawamoto, S., Hartman, B.: Introduction to Web services and their security. Information Security Technical Report 10, 2–14 (2005)
Li Jiewei, Z.C.: A Novel Automatic Parameters Optimization Approach Based on Differential Evolution for Support Vector Regression. In: Advances in Computation and Intelligence, pp. 510–519 (2008)
Feng, X., Tan, G.: Simplex search in the fusion of genetic algorithms. Computer Engineering and Applications 44(18), 30–33 (2008)
He, D., Jiang, L., Wang, F.-L.: Simplex operator based genetic algorithm. Information and Control 30(38), 276–278 (2001)
Chang, C.-C., Lin, C.-J.: Libsvm: a library for support vector machines (2003), http://www.csie.ntu.edu.tw/~cjlin/libsvm/
Dubois, G., Galmarini, S.: Introduction to the Spatial Interpolation Comparison (SIC) 2004 exercise and presentation of the data sets. Applied GIS 1(2), 09-1–09-11 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, D., Liu, W., Xu, X., Deng, Q. (2010). A Novel Interpolation Method Based on Differential Evolution-Simplex Algorithm Optimized Parameters for Support Vector Regression. In: Cai, Z., Hu, C., Kang, Z., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2010. Lecture Notes in Computer Science, vol 6382. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16493-4_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-16493-4_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16492-7
Online ISBN: 978-3-642-16493-4
eBook Packages: Computer ScienceComputer Science (R0)