전문가 시스템을 위한 실제적인 상황에서의 지식획득 방법

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 usually 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 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.