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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2010  >  10(2): 122-126
LI Dong-qin, WANG Li-zheng, GUAN Yi-feng, XU Hai-xiang. Parameter selection of support vector machine based on stepped-up chaos optimization algorithm[J]. Journal of Traffic and Transportation Engineering, 2010, 10(2): 122-126. doi: 10.19818/j.cnki.1671-1637.2010.02.022
Citation: LI Dong-qin, WANG Li-zheng, GUAN Yi-feng, XU Hai-xiang. Parameter selection of support vector machine based on stepped-up chaos optimization algorithm[J]. Journal of Traffic and Transportation Engineering, 2010, 10(2): 122-126. doi: 10.19818/j.cnki.1671-1637.2010.02.022
shu

Parameter selection of support vector machine based on stepped-up chaos optimization algorithm

doi: 10.19818/j.cnki.1671-1637.2010.02.022
  • 1.

    School of Naval Architecture and Ocean Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003, Jiangsu, China

  • 2.

    School of Transportation, Wuhan University of Technology, Wuhan 430063, Hubei, China

  • 3.

    Jiangsu Modern Shipbuilding Technology Ltd., Zhenjiang 212003, Jiangsu, China

More Information
  • Author Bio:

    LI Dong-qin(1979-), female, lectuer, PhD, +86-511-84401133, mandy-ldq@163.com

  • Received Date: 2009-12-22
  • Publish Date: 2010-04-25
    Abstract
  • In order to analyze the parameter selection of support vector machine (SVM), penalty coefficient, insensitive coefficient and width coefficient in radial basis function (RBF) were used as optimization variables, the former searching formula was changed, and the third searching time was added. A new improved stepped-up chaos optimization algorithm (ISCOA) was proposed by adopting the Chebyshev mapping instead of Logistic mapping to form initial chaos serial. The new algorithm was used in artificial data set and real data set, and was compared with traditional cross validation method. Test result indicates that the running time is cut down at least 23.43%, and the precision improves at least 6.31% by using ISCOA in artificial data set. The predicted value is more close to real value, and the relative errors are controlled under 3.13% in real data set. So ISCOA has higher prediction precision and optimization effect.

     

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