Evolutionary-based support vector machine

Author

Kuo, R.J. ; Chen, C.M.

Author_Institution

Dept. of Ind. Manage., Nat. Taiwan Univ. of Sci. & Technol., Taipei, Taiwan

fYear

2011

fDate

6-9 Dec. 2011

Firstpage

472

Lastpage

475

Abstract

This study proposed a hybrid of artificial immune system (AIS) and particle swarm optimization (PSO)-based support vector machine (SVM) (HIP-SVM) for optimizing SVM parameters. In order to evaluate the proposed HIP-SVM´s capability, six benchmark data sets, Australian, Heart disease, Iris, Ionosphere, Sonar and Vowel, were employed. The computational results showed that HIP-SVM has better performance than AIS-based SVM and PSO-based SVM.

Keywords

artificial immune systems; particle swarm optimisation; support vector machines; HIP-SVM; artificial immune system; evolutionary-based support vector machine; particle swarm optimization based support vector machine; Accuracy; Classification algorithms; Cloning; Immune system; Kernel; Particle swarm optimization; Support vector machines; Support vector machine; artificial immune sytem; evolutionary algorithms; particle swarm optimization;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Engineering and Engineering Management (IEEM), 2011 IEEE International Conference on

Conference_Location

Singapore

ISSN

2157-3611

Print_ISBN

978-1-4577-0740-7

Electronic_ISBN

2157-3611

Type

conf

DOI

10.1109/IEEM.2011.6117962

Filename

6117962

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2853890