Reducing complexity of decision trees with two variable tests

  • Pearson R. A. 
  • Smith E. K. T. 
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1160)


This paper examines some ways to reduce the compexity of built trees particularly with respect to disjunctive concepts.

A number of heuristics that allow two categorical (nominal) variables to be combined at each node are described. Different combinations of these heuristics are then applied to five data sets. When these cases are analysed it is found that a number of combinations perform better than or equal to the conventional partitioning techniques for nearly all the data sets. The only data set which doesn't perform well is the one which has attributes with a high arity. Future directions are then discussed raising the possibility of more (> 2) attributes tested at each node.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Pearson R. A. 
    • 1
  • Smith E. K. T. 
    • 2
  1. 1.University of New South Wales, A.D.F.A.CanberraAustralia
  2. 2.Computer ScienceAustralian National UniversityCanberrraAustralia

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