期刊
NEURAL COMPUTING & APPLICATIONS
卷 26, 期 3, 页码 713-721出版社
SPRINGER LONDON LTD
DOI: 10.1007/s00521-014-1757-z
关键词
Support vector machine; Gravitational search algorithm; Chaotic search; Parameter optimization; Feature selection
资金
- State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research [IWHR-SKL-201220]
- National Natural Science Foundation of China [51479076, 51109088, 51309258]
- Research Fund for the Doctoral Program of Higher Education of China [20110142120020]
- Fundamental Research Funds for the Central Universities, HUST [2013QN114]
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据