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A new optimizing parameter approach of LSSVM multiclass classification model

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Abstract

The parameter values of kernel function affect classification results to a certain extent. In the paper, a multiclass classification model based on improved least squares support vector machine (LSSVM) is presented. In the model, the non-sensitive loss function is replaced by quadratic loss function, and the inequality constraints are replaced by equality constraints. Consequently, quadratic programming problem is simplified as the problem of solving linear equation groups, and the SVM algorithm is realized by least squares method. When the LSSVM is used in multiclass classification, it is presented to choose parameter of kernel function on dynamic, which enhances preciseness rate of classification. The Fibonacci symmetry searching algorithm is simplified and improved. The changing rule of kernel function searching region and best shortening step is studied. The best multiclass classification results are obtained by means of synthesizing kernel function searching region and best shortening step. The simulation results show the validity of the model.

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Acknowledgments

This work is supported by China Postdoctoral Science Foundation (2005038515) and the Science Foundation of Hebei University of Science and Technology (XL2006081).

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Authors and Affiliations

  1. School of Information, Hebei University of Science and Technology, Shijiazhuang, 050018, China

    Kui He Yang & Ling Ling Zhao

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  1. Kui He Yang
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  2. Ling Ling Zhao
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Correspondence to Kui He Yang.

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Yang, K.H., Zhao, L.L. A new optimizing parameter approach of LSSVM multiclass classification model. Neural Comput & Applic 21, 945–955 (2012). https://doi.org/10.1007/s00521-011-0673-8

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