• DocumentCode
    499074
  • Title

    Tree classifier in spectral space

  • Author

    He, Ping ; Xu, Xiao-huax ; Chen, Ling

  • Author_Institution
    Dept. of Comput. Sci. & Eng., Nanjing Univ. of Aeronaut. & Astronaut., Nanjing, China
  • Volume
    1
  • fYear
    2009
  • fDate
    12-15 July 2009
  • Firstpage
    476
  • Lastpage
    481
  • Abstract
    This paper proposes a novel nonlinear decision tree algorithm SSDT, spectral space decision tree. SSDT adopts spectral space transformation to extract the cluster information of data, employs decision tree to discover the decision boundary, and classifies test data with consistent mapping principle. Experimental results show that SSDT can produce higher classification accuracy and better generalization ability than the traditional decision tree algorithms.
  • Keywords
    decision trees; information retrieval; pattern classification; cluster information extraction; consistent mapping principle; data classification; nonlinear decision tree algorithm; spectral space decision tree; spectral space transformation; tree classifier; Classification tree analysis; Clustering algorithms; Computer science; Cybernetics; Data mining; Decision trees; Machine learning; Partitioning algorithms; Testing; Training data; Nonlinear tree classifier; Spectral space;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2009 International Conference on
  • Conference_Location
    Baoding
  • Print_ISBN
    978-1-4244-3702-3
  • Electronic_ISBN
    978-1-4244-3703-0
  • Type

    conf

  • DOI
    10.1109/ICMLC.2009.5212576
  • Filename
    5212576
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=499074