• DocumentCode
    2004512
  • Title

    Efficient feature selection using a self-adjusting harmony search algorithm

  • Author

    Ling Zheng ; Ren Diao ; Qiang Shen

  • Author_Institution
    Dept. of Comput. Sci., Aberystwyth Univ., Aberystwyth, UK
  • fYear
    2013
  • fDate
    9-11 Sept. 2013
  • Firstpage
    167
  • Lastpage
    174
  • Abstract
    Many strategies have been exploited for the task of feature selection, in an effort to identify more compact and better quality subsets. The use of an evaluation metric have been developed recently that can judge the quality of a given subset as a whole, rather than a combination of individual features. Powerful nature-inspired stochastic search techniques have also emerged, allowing multiple good quality features to be discovered without resorting to exhaustive search. Harmony search in particular, is a recently developed technique that mimics musicians´ experience, which has been successfully applied to solving feature selection problems. This paper proposes three improvements to the harmony search algorithm that are designed to further enhance its feature selection performance. The resultant technique is more efficient, capable of automatically adjusting the internal components of the algorithm. Systematic experimental evaluation using high dimensional, real-valued data sets has been carried out to verify the benefits of the presented work.
  • Keywords
    data handling; feature extraction; search problems; stochastic processes; evaluation metric; exhaustive search; feature combination; feature discoverty; feature selection performance; feature selection problem; high dimensional real-valued data set; musician experience; nature-inspired stochastic search technique; self-adjusting harmony search algorithm; Accuracy; Convergence; Feature extraction; Heuristic algorithms; Optimization; Sonar; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence (UKCI), 2013 13th UK Workshop on
  • Conference_Location
    Guildford
  • Print_ISBN
    978-1-4799-1566-8
  • Type

    conf

  • DOI
    10.1109/UKCI.2013.6651302
  • Filename
    6651302