DocumentCode
263465
Title
Feature Selection of Support Vector Machine Based on Harmonious Cat Swarm Optimization
Author
Kuan Cheng Lin ; Kai Yuan Zhang ; Hung, Jason C.
Author_Institution
Dept. of Manage., Nat. Chung Hsing Univ., Taichung, Taiwan
fYear
2014
fDate
12-14 July 2014
Firstpage
205
Lastpage
208
Abstract
Cat Swarm Optimization Algorithm (CSO) is an optimization algorithm which proposed in 2006. Indicated by previous studies, CSO has good performance. We proposed a method to improve CSO and presenting a modified CSO named Harmonious-CSO (HCSO). The method is changing the concept of cat alert surroundings in seeking mode of CSO. We change the formula of seeking mode and add a concept of HS algorithm. In this paper, we use Support Vector Machine (SVM) be classifier combine with feature selection to verify the performance of algorithm. For the experimental results, the HCSO algorithm has a better solution than CSO.
Keywords
feature selection; particle swarm optimisation; search problems; support vector machines; HCSO algorithm; HS algorithm; SVM; feature selection; harmonious cat swarm optimization algorithm; modified CSO algorithm; support vector machine; Accuracy; Algorithm design and analysis; Cats; Classification algorithms; Optimization; Particle swarm optimization; Support vector machines; SVM; cat swarm optimization; feature selection; harmony search algorithm;
fLanguage
English
Publisher
ieee
Conference_Titel
Ubi-Media Computing and Workshops (UMEDIA), 2014 7th International Conference on
Conference_Location
Ulaanbaatar
Print_ISBN
978-1-4799-4267-1
Type
conf
DOI
10.1109/U-MEDIA.2014.38
Filename
6916353
Link To Document