An Empirical Comparison of Pruning Methods for Decision Tree Induction

Mingers, John (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)

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Official URL
http://dx.doi.org/10.1023/A:1022604100933

Abstract

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.

Item Type: Article
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: 02 Jun 2014 10:26
Resource URI: http://kar.kent.ac.uk/id/eprint/3786 (The current URI for this page, for reference purposes)
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