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Modified Cat Swarm Optimization Algorithm for Feature Selection of Support Vector Machines

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Frontier and Innovation in Future Computing and Communications

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 301))

Abstract

Cat swarm optimization (CSO) is a novel meta-heuristic for evolutionary optimization algorithms based on swarm intelligence. CSO imitates the behavior of cats through two sub-modes: seeking and tracing. Previous studies have indicated that CSO algorithms outperform other well-known meta-heuristics, such as genetic algorithms and particle swarm optimization. This study presents a modified version of cat swarm optimization (MCSO), capable of improving search efficiency within the problem space. The basic CSO algorithm was integrated with a local search procedure as well as the feature selection of support vector machines (SVMs). Experimental results demonstrate that the proposed MCSO algorithm provides better results in less time than basic CSO algorithms.

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Author information

Authors and Affiliations

  1. Department of Management Information Systems, National Chung Hsing University, Taichung, Taiwan

    Kuan-Cheng Lin & Yung-Tso Lin

  2. Department of Mathematics Education, National Taichung University of Education, Taichung, Taiwan

    Yi-Hung Huang

  3. Department of Information Management, Overseas Chinese University, Taichung, Taiwan

    Jason C. Hung

Authors
  1. Kuan-Cheng Lin
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  2. Yi-Hung Huang
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  3. Jason C. Hung
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  4. Yung-Tso Lin
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Seoul University of Science & and Technology (SeoulTech), Seoul, Korea, Republic of (South Korea)

    James J. (Jong Hyuk) Park

  2. School of Information Technologies, University of Sydney, Sydney, New South Wales, Australia

    Albert Zomaya

  3. Humanitas College, Kyung Hee University, Seoul, Korea, Republic of (South Korea)

    Hwa-Young Jeong

  4. Computer Science & Software Engineering, Monmouth University, W. Long Branch, New Jersey, USA

    Mohammad Obaidat

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© 2014 Springer Science+Business Media Dordrecht

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Lin, KC., Huang, YH., Hung, J.C., Lin, YT. (2014). Modified Cat Swarm Optimization Algorithm for Feature Selection of Support Vector Machines. In: Park, J., Zomaya, A., Jeong, HY., Obaidat, M. (eds) Frontier and Innovation in Future Computing and Communications. Lecture Notes in Electrical Engineering, vol 301. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-8798-7_40

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  • DOI: https://doi.org/10.1007/978-94-017-8798-7_40

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  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-017-8797-0

  • Online ISBN: 978-94-017-8798-7

  • eBook Packages: EngineeringEngineering (R0)

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