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Please use this identifier to cite or link to this item: http://hdl.handle.net/10651/29971

Title: A heuristic for learning decision trees and pruning them into classification rules
Author(s): Ranilla Pastor, José
Luaces Rodríguez, Óscar
Bahamonde Rionda, Antonio
Keywords: Classification rules
Decision trees
Impurity level
Machine learning
Issue date: 2003
Publisher: IOS Press
Citation: AI Communications, 16(2), p. 71-87 (2003)
Format extent: p. 71-87
Abstract: Let us consider a set of training examples described by continuous or symbolic attributes with categorical classes. In this paper we present a measure of the potential quality of a region of the attribute space to be represented as a rule condition to classify unseen cases. The aim is to take into account the distribution of the classes of the examples. The resulting measure, called impurity level, is inspired by a similar measure used in the instance-based algorithm IB3 for selecting suitable paradigmatic exemplars that will classify, in a nearest-neighbor context, future cases. The features of the impurity level are illustrated using a version of Quinlan's well-known C4.5 where the information-based heuristics are replaced by our measure. The experiments carried out to test the proposals indicate a very high accuracy reached with sets of classification rules as small as those found by RIPPER
URI: http://hdl.handle.net/10651/29971
ISSN: 0921-7126
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