Multi-interval discretization methods for decision tree learning

  • Petra Perner
  • Sascha Trautzsch
Poster Papers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1451)


Properly addressing the discretization process of continuos valued features is an important problem during decision tree learning. This paper describes four multi-interval discretization methods for induction of decision trees used in dynamic fashion. We compare two known discretization methods to two new methods proposed in this paper based on a histogram based method and a neural net based method (LVQ). We compare them according to accuracy of the resulting decision tree and to compactness of the tree. For our comparison we used three data bases, IRIS domain, satellite domain and OHS domain (ovariel hyper stimulation).


Discretization Method Learn Vector Quantization Decision Tree Learning Entropy Criterion Minimal Description Length Principle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Petra Perner
    • 1
  • Sascha Trautzsch
    • 1
  1. 1.Institute of Computer Vision and Applied Computer Sciences e.V.LeipzigGermany

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