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Theory and practice of decision tree induction

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  • Kim, H.
  • Koehler, G. J.

Abstract

Induction methods have recently been found to be useful in a wide variety of business related problems, including in the construction of expert systems. Decision tree induction is an important type of inductive learning method. Empirical results have shown that pruning a decision tree sometimes improves its accuracy. In this paper we summarize theoretical results of pruning and illustrate these results with an example. We give a sample size sufficient for decision tree induction with pruning based on recently developed learning theory. For situations where it is difficult to obtain a large enough sample, we provide several methods for a posterior evaluation of the accuracy of a pruned decision tree. Finally we summarize conditions under which pruning is necessary for better prediction accuracy.

Suggested Citation

  • Kim, H. & Koehler, G. J., 1995. "Theory and practice of decision tree induction," Omega, Elsevier, vol. 23(6), pages 637-652, December.
    • Handle: RePEc:eee:jomega:v:23:y:1995:i:6:p:637-652
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    References listed on IDEAS

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    1. Kim, Hyunsoo & Koehler, Gary J., 1994. "An investigation on the conditions of pruning an induced decision tree," European Journal of Operational Research, Elsevier, vol. 77(1), pages 82-95, August.
    2. William F. Messier, Jr. & James V. Hansen, 1988. "Inducing Rules for Expert System Development: An Example Using Default and Bankruptcy Data," Management Science, INFORMS, vol. 34(12), pages 1403-1415, December.
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