caa a22 4500 606159657 CHVBK 20210128100625.0 cr unu---uuuuu 210128e20150401xx s 000 0 eng 10.1007/s00521-014-1757-z doi (NATIONALLICENCE)springer-10.1007/s00521-014-1757-z A chaos embedded GSA-SVM hybrid system for classification [Elektronische Daten] [Chaoshun Li, Xueli An, Ruhai Li] 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. The Natural Computing Applications Forum, 2014 Support vector machine nationallicence Gravitational search algorithm nationallicence Chaotic search nationallicence Parameter optimization nationallicence Feature selection nationallicence Li Chaoshun State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, 100044, Beijing, China aut An Xueli State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, 100044, Beijing, China aut Li Ruhai School of Hydropower and Information Engineering, Huazhong University of Science and Technology, 430074, Wuhan, China aut Neural Computing and Applications Springer London 26/3(2015-04-01), 713-721 0941-0643 26:3<713 2015 26 521 https://doi.org/10.1007/s00521-014-1757-z text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-014-1757-z text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Li Chaoshun State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, 100044, Beijing, China aut NATIONALLICENCE 700 1- An Xueli State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, 100044, Beijing, China aut NATIONALLICENCE 700 1- Li Ruhai School of Hydropower and Information Engineering, Huazhong University of Science and Technology, 430074, Wuhan, China aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/3(2015-04-01), 713-721 0941-0643 26:3<713 2015 26 521