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A chaos embedded GSA-SVM hybrid system for classification

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Abstract

Parameter optimization and feature selection influence the classification accuracy of support vector machine (SVM) significantly. In order to improve classification accuracy of SVM, this paper hybridizes chaotic search and gravitational search algorithm (GSA) with SVM and presents a new chaos embedded GSA-SVM (CGSA-SVM) hybrid system. In this system, input feature subsets and the SVM parameters are optimized simultaneously, while GSA is used to optimize the parameters of SVM and chaotic search is embedded in the searching iterations of GSA to optimize the feature subsets. Fourteen UCI datasets are employed to calculate the classification accuracy rate in order to evaluate the developed CGSA-SVM approach. The developed approach is compared with grid search and some other hybrid systems such as GA-SVM, PSO-SVM and GSA-SVM. The results show that the proposed approach achieves high classification accuracy and efficiency compared with well-known similar classifier systems.

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Acknowledgments

This paper is supported by the Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin,China Institute of Water Resources and Hydropower Research (Grant No. IWHR-SKL-201220), the National Natural Science Foundation of China (Grant Nos. 51479076, 51109088, 51309258), the Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20110142120020) and the Fundamental Research Funds for the Central Universities, HUST (No. 2013QN114).

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Authors and Affiliations

  1. State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, 100044, China

    Chaoshun Li & Xueli An

  2. School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Chaoshun Li & Ruhai Li

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  1. Chaoshun Li
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  2. Xueli An
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  3. Ruhai Li
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Correspondence to Chaoshun Li.

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Li, C., An, X. & Li, R. A chaos embedded GSA-SVM hybrid system for classification. Neural Comput & Applic 26, 713–721 (2015). https://doi.org/10.1007/s00521-014-1757-z

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  • DOI: https://doi.org/10.1007/s00521-014-1757-z

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