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dc.contributor.authorDuman, Ekrem
dc.contributor.authorElikucuk, I.
dc.date.accessioned2014-11-25T07:31:56Z
dc.date.available2014-11-25T07:31:56Z
dc.date.issued2013
dc.identifier.isbn978-3-642-38682-4
dc.identifier.urihttp://link.springer.com/chapter/10.1007/978-3-642-38682-4_8
dc.identifier.urihttp://hdl.handle.net/10679/671
dc.descriptionDue to copyright restrictions, the access to the full text of this article is only available via subscription.en_US
dc.description.abstractStatistical fraud detection problem is a very difficult problem in that there are very few examples of fraud. The great majority of transactions are legitimate. On the other hand, for this binary classification problem the costs of the two types of classification errors (FP=false positive and FN=false negative) are not the same. Thus, the classical data mining algorithms do not fit to the problem exactly. Departing from this fact, we have solved this problem by genetic algorithms and scatter search. Now, we apply the recently developed new metaheuristics algorithm namely the migrating birds optimization algorithm (MBO) to this problem. Results show that it outperforms the former approach. The performance of standard MBO is further increased by the help of some modified benefit mechanisms.en_US
dc.language.isoengen_US
dc.publisherSpringer Science+Business Mediaen_US
dc.relation.ispartofAdvances in Computational Intelligence
dc.rightsrestrictedAccess
dc.titleSolving credit card fraud detection problem by the new metaheuristics migrating birds optimizationen_US
dc.typeConference paperen_US
dc.peerreviewedyesen_US
dc.publicationstatuspublisheden_US
dc.contributor.departmentÖzyeğin University
dc.contributor.authorID(ORCID 0000-0001-5176-6186 & YÖK ID 142351) Duman, Ekrem
dc.contributor.ozuauthorDuman, Ekrem
dc.identifier.volume7903
dc.identifier.startpage62
dc.identifier.endpage71
dc.identifier.wosWOS:000324899200008
dc.identifier.doi10.1007/978-3-642-38682-4_8
dc.subject.keywordsMigrating birds optimization algorithmen_US
dc.subject.keywordsFrauden_US
dc.subject.keywordsCredit cardsen_US
dc.subject.keywordsGenetic algorithmsen_US
dc.identifier.scopusSCOPUS:2-s2.0-84880057301
dc.contributor.authorMale1
dc.relation.publicationcategoryConference Paper - International - Institutional Academic Staff


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