search query: @author Kim, H. / total: 13
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| Author: | Kim, H. Koehler, G. |
| Title: | An investigation on the conditions of pruning an induced decision tree |
| Journal: | European Journal of Operational Research
1994 : AUG 25, VOL. 77:1, p. 82-95 |
| Index terms: | DECISION TREES FLEXIBLE WORKING HOURS ARTIFICIAL INTELLIGENCE |
| Language: | eng |
| Abstract: | Empirical studies have shown that pruning a decision tree can increase the accuracy of a learned concept. A recent result identified conditions under which pruning techniques increase prediction accuracy. However, this result is based on samples of size three. This paper provides a generalization of previous results and investigates conditions where pruning is beneficial for concept accuracy as well as concept simplification. The authors show that pruning is theoretically useful in many situations. |
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