• DocumentCode
    2139175
  • Title

    An Experimental Study on Feature Subset Selection Methods

  • Author

    Yun, Chulmin ; Shin, Donghyuk ; Jo, Hyunsung ; Yang, Jihoon ; Kim, Saejoon

  • Author_Institution
    Sogang Univ., Seoul
  • fYear
    2007
  • fDate
    16-19 Oct. 2007
  • Firstpage
    77
  • Lastpage
    82
  • Abstract
    In the field of machine learning and pattern recognition, feature subset selection is an important area, where many approaches have been proposed. In this paper, we choose some feature selection algorithms and analyze their performance using various datasets from public domain. We measured the number of reduced features and the improvement of learning performance with chosen feature selection methods, then evaluated and compared each method on the basis of these measurements.
  • Keywords
    learning (artificial intelligence); feature subset selection methods; learning performance; machine learning; pattern recognition; Algorithm design and analysis; Computational efficiency; Computer science; Costs; Information technology; Learning systems; Machine learning; Machine learning algorithms; Pattern recognition; Performance analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer and Information Technology, 2007. CIT 2007. 7th IEEE International Conference on
  • Conference_Location
    Aizu-Wakamatsu, Fukushima
  • Print_ISBN
    978-0-7695-2983-7
  • Type

    conf

  • DOI
    10.1109/CIT.2007.81
  • Filename
    4385060