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Neural Computing and Applications

Erschienen in:

01.04.2015 | Original Article

A chaos embedded GSA-SVM hybrid system for classification

verfasst von: Chaoshun Li, Xueli An, Ruhai Li

Erschienen in: Neural Computing and Applications | Ausgabe 3/2015

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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.

Vorheriger Artikel Soft variable structure controller design for constrained systems based on S-class functions

Nächster Artikel Black box modeling of PIDs implemented in PLCs without structural information: a support vector regression approach

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Titel: A chaos embedded GSA-SVM hybrid system for classification
verfasst von: Chaoshun Li
Xueli An
Ruhai Li
Publikationsdatum: 01.04.2015
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 3/2015
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-014-1757-z

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