Machine Learning

, Volume 11, Issue 1, pp 7–22

Coding Decision Trees

  • C.S. Wallace
  • J.D. Patrick
Article

Abstract

Quinlan and Rivest have suggested a decision-tree inference method using the Minimum Description Length idea. We show that there is an error in their derivation of message lengths, which fortunately has no effect on the final inference. We further suggest two improvements to their coding techniques, one removing an inefficiency in the description of non-binary trees, and one improving the coding of leaves. We argue that these improvements are superior to similarly motivated proposals in the original paper.

Empirical tests confirm the good results reported by Quinlan and Rivest, and show our coding proposals to lead to useful improvements in the performance of the method.

decision trees supervised learning minimum message length minimum description length information theory 

Copyright information

© Kluwer Academic Publishers 1993

Authors and Affiliations

  • C.S. Wallace
    • 1
  • J.D. Patrick
    • 2
  1. 1.Computer ScienceMonash UniversityClayton
  2. 2.Computing & MathematicsDeakin UniversityGeelongAustralia

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