search query: @author Kim, H. / total: 13
reference: 10 / 13
| Author: | Kim, H. Koehler, G. |
| Title: | Theory and practice of decision tree induction |
| Journal: | Omega
1995 : DEC, VOL. 23:6, p. 637-652 |
| Index terms: | KNOWLEDGE-BASED SYSTEMS DECISION TREES THEORIES |
| Language: | eng |
| 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, the authors summarize theoretical results of pruning and illustrate these results with an example. The authors give a sample size sufficient for decision tree induction with pruning based on recently developed learning theory. |
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