• 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