Mingers, J. (1989) An Empirical Comparison of Pruning Methods for Decision Tree Induction. Machine Learning, 4 (2). pp. 227-243. ISSN 0885-6125.
|The full text of this publication is not available from this repository. (Contact us about this Publication)|
This paper compares five methods for pruning decision trees, developed from sets of examples. When used with uncertain rather than deterministic data, decision-tree induction involves three main stages—creating a complete tree able to classify all the training examples, pruning this tree to give statistical reliability, and processing the pruned tree to improve understandability. This paper concerns the second stage—pruning. It presents empirical comparisons of the five methods across several domains. The results show that three methods—critical value, error complexity and reduced error—perform well, while the other two may cause problems. They also show that there is no significant interaction between the creation and pruning methods.
|Uncontrolled keywords:||Decision trees, Knowledge acquisition, Uncertain data, Pruning|
|Subjects:||H Social Sciences > HD Industries. Land use. Labor > HD29 Operational Research - Applications|
|Divisions:||Faculties > Social Sciences > Kent Business School|
|Depositing User:||John Mingers|
|Date Deposited:||09 Sep 2009 18:56|
|Last Modified:||14 Jan 2010 14:13|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/3786 (The current URI for this page, for reference purposes)|
- Depositors only (login required):