• DocumentCode
    3431438
  • Title

    Research on grain information classification based on SVM decision tree

  • Author

    Geng, Ruihuan ; Zhang, Dexian ; Chai, Jiajia

  • Author_Institution
    College of Information Science and Engineering, Henan University of Technology, Zhengzhou, China
  • fYear
    2012
  • fDate
    11-13 Aug. 2012
  • Firstpage
    138
  • Lastpage
    141
  • Abstract
    The defections of traditional support vector machine (for short SVM) are analyzed in the paper. According to the characteristics of grain information on the web, a multi-class classification method based on SVM decision tree (for short SVM-DT) is presented for grain information classification. Experiments prove that F1-Measure values for SVM-DT algorithm is superior to the traditional SVM algorithm. It is more suitable for application to grain information classification system.
  • Keywords
    Frequency measurement; Noise; Support vector machines; Grain information; SVM Binary tree; Web text mining;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Granular Computing (GrC), 2012 IEEE International Conference on
  • Conference_Location
    Hangzhou, China
  • Print_ISBN
    978-1-4673-2310-9
  • Type

    conf

  • DOI
    10.1109/GrC.2012.6468622
  • Filename
    6468622