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A heuristic for learning decision trees and pruning them into classification rules
dc.contributor.author | Ranilla Pastor, José | |
dc.contributor.author | Luaces Rodríguez, Óscar | |
dc.contributor.author | Bahamonde Rionda, Antonio | |
dc.date.accessioned | 2015-03-03T10:17:12Z | |
dc.date.available | 2015-03-03T10:17:12Z | |
dc.date.issued | 2003 | |
dc.identifier.citation | AI Communications, 16(2), p. 71-87 (2003) | |
dc.identifier.issn | 0921-7126 | |
dc.identifier.uri | http://hdl.handle.net/10651/29971 | |
dc.description.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 | |
dc.format.extent | p. 71-87 | spa |
dc.language.iso | eng | spa |
dc.publisher | IOS Press | |
dc.relation.ispartof | AI Communications, 16(2) | spa |
dc.rights | © IOS Press | |
dc.rights | CC Reconocimiento - No comercial - Sin obras derivadas 3.0 España | |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | |
dc.subject | Classification rules | |
dc.subject | Decision trees | |
dc.subject | Impurity level | |
dc.subject | Machine learning | |
dc.subject | Pruning | |
dc.title | A heuristic for learning decision trees and pruning them into classification rules | eng |
dc.type | journal article | |
dc.rights.accessRights | open access |
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