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dc.contributor.authorQabajeh, Issaen
dc.contributor.authorThabtaha, Fadien
dc.contributor.authorChiclana, Franciscoen
dc.date.accessioned2015-10-19T08:42:42Z
dc.date.available2015-10-19T08:42:42Z
dc.date.issued2015-10
dc.identifier.citationQabajehb, I., Thabtaha, F. and Chiclana, F. (2015) A dynamic rule-induction method for classification in data mining. Journal of Management Analytics, 2 (3), pp. 233-253en
dc.identifier.urihttp://hdl.handle.net/2086/11278
dc.description.abstractRule induction (RI) produces classifiers containing simple yet effective ‘If–Then' rules for decision makers. RI algorithms normally based on PRISM suffer from a few drawbacks mainly related to rule pruning and rule-sharing items (attribute values) in the training data instances. In response to the above two issues, a new dynamic rule induction (DRI) method is proposed. Whenever a rule is produced and its related training data instances are discarded, DRI updates the frequency of attribute values that are used to make the next in-line rule to reflect the data deletion. Therefore, the attribute value frequencies are dynamically adjusted each time a rule is generated rather statically as in PRISM. This enables DRI to generate near perfect rules and realistic classifiers. Experimental results using different University of California Irvine data sets show competitive performance in regards to error rate and classifier size of DRI when compared to other RI algorithms.en
dc.language.isoen_USen
dc.publisherTaylor & Francis Groupen
dc.subjectdata miningen
dc.subjectclassification rulesen
dc.subjectrule inductionen
dc.subjectexpected accuracyen
dc.titleA dynamic rule-induction method for classification in data miningen
dc.typeArticleen
dc.identifier.doihttps://dx.doi.org/10.1080/23270012.2015.1090889
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderNAen
dc.projectidNAen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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