Please use this identifier to cite or link to this item: https://doi.org/10.1007/s10589-005-3907-9
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dc.titleA dual-objective evolutionary algorithm for rules extraction in data mining
dc.contributor.authorTan, K.C.
dc.contributor.authorYu, Q.
dc.contributor.authorAng, J.H.
dc.date.accessioned2014-06-16T09:27:03Z
dc.date.available2014-06-16T09:27:03Z
dc.date.issued2006-06
dc.identifier.citationTan, K.C.,Yu, Q.,Ang, J.H. (2006-06). A dual-objective evolutionary algorithm for rules extraction in data mining. Computational Optimization and Applications 34 (2) : 273-294. ScholarBank@NUS Repository. <a href="https://doi.org/10.1007/s10589-005-3907-9" target="_blank">https://doi.org/10.1007/s10589-005-3907-9</a>
dc.identifier.issn09266003
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/54099
dc.description.abstractThis paper presents a dual-objective evolutionary algorithm (DOEA) for extracting multiple decision rule lists in data mining, which aims at satisfying the classification criteria of high accuracy and ease of user comprehension. Unlike existing approaches, the algorithm incorporates the concept of Pareto dominance to evolve a set of non-dominated decision rule lists each having different classification accuracy and number of rules over a specified range. The classification results of DOEA are analyzed and compared with existing rule-based and non-rule based classifiers based upon 8 test problems obtained from UCI Machine Learning Repository. It is shown that the DOEA produces comprehensible rules with competitive classification accuracy as compared to many methods in literature. Results obtained from box plots and t-tests further examine its invariance to random partition of datasets. © 2006 Springer + Business Media, Inc.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/s10589-005-3907-9
dc.sourceScopus
dc.subjectClassification
dc.subjectData mining
dc.subjectEvolutionary algorithm
dc.subjectRules extraction
dc.typeArticle
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1007/s10589-005-3907-9
dc.description.sourcetitleComputational Optimization and Applications
dc.description.volume34
dc.description.issue2
dc.description.page273-294
dc.description.codenCPPPE
dc.identifier.isiutNOT_IN_WOS
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