Title :
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
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;
Conference_Titel :
Industrial Engineering and Engineering Management (IEEM), 2011 IEEE International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4577-0740-7
Electronic_ISBN :
2157-3611
DOI :
10.1109/IEEM.2011.6117962