A dynamic rule-induction method for classification in data mining

dc.contributor.authorQabajeh, Issa
dc.contributor.authorChiclana, Francisco
dc.contributor.authorThabtah, Fadi
dc.date.accessioned2020-02-23T14:35:48Z
dc.date.available2020-02-23T14:35:48Z
dc.date.copyright2015
dc.date.issued2015
dc.descriptionThis article is not available at CUD collection. The version of scholarly record of this Article is published in Journal of Management Analytics (2015), available online at: https://doi.org/10.1080/23270012.2015.1090889.en_US
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. © 2015, © 2015 Antai College of Economics and Management, Shanghai Jiao Tong University.en_US
dc.identifier.citationQabajeh, I., Thabtah, F., & Chiclana, F. (2015). A dynamic rule-induction method for classification in data mining. Journal of Management Analytics, 2(3), 233–253. https://doi.org/10.1080/23270012.2015.1090889en_US
dc.identifier.issn23270012
dc.identifier.urihttp://dx.doi.org/10.1080/23270012.2015.1090889
dc.identifier.urihttp://hdl.handle.net/20.500.12519/155
dc.language.isoenen_US
dc.publisherTaylor and Francis Ltd.en_US
dc.relationAuthors Affiliations: Qabajeh, I., E-Business Department, Canadian University of Dubai, Dubai, United Arab Emirates; Thabtah, F., Computing and Informatics Department, De Montfort University, Leicester, United Kingdom; Chiclana, F., Centre for Computational Intelligence, De Montfort University, Leicester, United Kingdom
dc.relation.ispartofseriesJournal of Management Analytics;Vol. 2, no. 3
dc.rightsPermission to reuse the abstract has been secured from Taylor and Francis Ltd..
dc.rights.holderCopyright : 2015 Antai College of Economics and Management, Shanghai Jiao Tong University
dc.subjectClassification rulesen_US
dc.subjectData miningen_US
dc.subjectExpected accuracyen_US
dc.subjectRule inductionen_US
dc.titleA dynamic rule-induction method for classification in data miningen_US
dc.typeArticleen_US

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