• DocumentCode
    2341087
  • Title

    Cline: new multivariate decision tree construction heuristics

  • Author

    Amasyali, M. Fatih ; Ersoy, Okan

  • Author_Institution
    Dept. of Comput. Eng., Yildiz Tech. Univ., Istanbul
  • fYear
    0
  • fDate
    0-0 0
  • Abstract
    Decision trees are often used in pattern recognition and regression problems. They are attractive due to high performance and easy-to-understand rules. Many different decision tree construction algorithms have been developed because of their popularity. In this work, we describe some new heuristic tree construction algorithms and test with 8 benchmark datasets. We compare the new method with other 21 tree induction algorithms. The results show that cline heuristics can be used in all types of classification problems because of its simplicity and acceptable performance
  • Keywords
    decision trees; Cline; decision tree construction algorithms; heuristic tree construction algorithms; multivariate decision tree construction heuristics; pattern recognition; regression problems; tree induction algorithms; Benchmark testing; Binary trees; Classification tree analysis; Decision making; Decision trees; Machine learning; Machine learning algorithms; Navigation; Pattern recognition; Regression tree analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence Methods and Applications, 2005 ICSC Congress on
  • Conference_Location
    Istanbul
  • Print_ISBN
    1-4244-0020-1
  • Type

    conf

  • DOI
    10.1109/CIMA.2005.1662359
  • Filename
    1662359