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Front. Inform. Technol. Electron. Eng.  2011, Vol. 12 Issue (4): 297-306    DOI: 10.1631/jzus.C1000110
    
Extremal optimization for optimizing kernel function and its parameters in support vector regression
Peng Chen*,1, Yong-zai Lu2
1 Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China 2 Department of Automation, Zhejiang University, Hangzhou 310027, China
Extremal optimization for optimizing kernel function and its parameters in support vector regression
Peng Chen*,1, Yong-zai Lu2
1 Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China 2 Department of Automation, Zhejiang University, Hangzhou 310027, China
 全文: PDF(407 KB)  
摘要： The performance of the support vector regression (SVR) model is sensitive to the kernel type and its parameters. The determination of an appropriate kernel type and the associated parameters for SVR is a challenging research topic in the field of support vector learning. In this study, we present a novel method for simultaneous optimization of the SVR kernel function and its parameters, formulated as a mixed integer optimization problem and solved using the recently proposed heuristic ‘extremal optimization (EO)’. We present the problem formulation for the optimization of the SVR kernel and parameters, the EO-SVR algorithm, and experimental tests with five benchmark regression problems. The results of comparison with other traditional approaches show that the proposed EO-SVR method provides better generalization performance by successfully identifying the optimal SVR kernel function and its parameters.
关键词： Support vector regression (SVR);  Extremal optimization (EO);  Parameter optimization;  Kernel function optimization    
Abstract: The performance of the support vector regression (SVR) model is sensitive to the kernel type and its parameters. The determination of an appropriate kernel type and the associated parameters for SVR is a challenging research topic in the field of support vector learning. In this study, we present a novel method for simultaneous optimization of the SVR kernel function and its parameters, formulated as a mixed integer optimization problem and solved using the recently proposed heuristic ‘extremal optimization (EO)’. We present the problem formulation for the optimization of the SVR kernel and parameters, the EO-SVR algorithm, and experimental tests with five benchmark regression problems. The results of comparison with other traditional approaches show that the proposed EO-SVR method provides better generalization performance by successfully identifying the optimal SVR kernel function and its parameters.
Key words: Support vector regression (SVR)    Extremal optimization (EO)    Parameter optimization    Kernel function optimization
收稿日期: 2010-04-20 出版日期: 2011-04-11
CLC:  TP181  
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引用本文:

Peng Chen, Yong-zai Lu. Extremal optimization for optimizing kernel function and its parameters in support vector regression. Front. Inform. Technol. Electron. Eng., 2011, 12(4): 297-306.

链接本文:

http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C1000110    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2011/V12/I4/297

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