Robust estimation of support vector regression via residual bootstrap adoption
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As current system designs grow increasingly complex and expensive to analyze, the need for design optimization has also grown. In this study, a more stable approximation model is proposed via the application of a bootstrap to support vector regression (SVR). SVR expresses the nonlinearity of the system relatively well. However, using SVR does not always guarantee accurate results because it is sensitive to the input parameters. To overcome this drawback, we apply a bootstrap to SVR, using the residual from SVR as the bootstrap. The performance of the proposed method is evaluated via application to numerical examples and a real problem. We observed that the proposed method not only produced valuable results but also noticeably eliminated the negative effects of input parameters.
KeywordsSupport vector regression Bootstrap Residual Root median square error
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- T. Wenhu, S. Almas and Q. H. Wu, Transformer dissolved gas analysis using least square support vector machine and bootstrap, Proceedings of the 26th Chinese Control Conference (2007) 482–486.Google Scholar
- D. Kim and S. Cho, Bootstrap based pattern selection for support vector regression, Advances in Knowledge Discovery and Data Mining, Lecture Notes in Computer Science, 5012 (2008) 608–615.Google Scholar
- P. Good, Permutation, Parametric, and Bootstrap Tests of Hypotheses, Third Edition, Springer (2004).Google Scholar
- J. H. Kim, The bootstrap confidence interval in regression model with non-normal error, Thesis (M.A.), Hanyang University, Korea (1999).Google Scholar
- B. E. Boser, I. M. Guyon and V. Vapnik, A training algorithm for optimum margin classifiers, In Fifth Annual Workshop on Computational Learning Theory (1992) 144–152.Google Scholar
- PIAnO (Process Integration, Automation and Optimization) User’s Manual, Version 3.5, PIDOTECH Inc. (2013).Google Scholar
- R 2.13.1, http://cran.r-project.org.