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dc.contributor.authorZakaryazad, Ashkan
dc.contributor.authorDuman, Ekrem
dc.contributor.authorKibekbaev, Azamat
dc.date.accessioned2016-02-16T10:26:11Z
dc.date.available2016-02-16T10:26:11Z
dc.date.issued2015
dc.identifier.urihttp://hdl.handle.net/10679/2646
dc.descriptionDue to copyright restrictions, the access to the full text of this article is only available via subscription.
dc.description.abstractA typical classification technique ranks the instances in a data set according to the likelihood of belonging to one (positive) class. A credit card (CC) fraud detection model ranks the transactions in terms of probability of being fraud. In fact, this approach is often criticized, because firms do not care about fraud probability but about the profitability or costliness of detecting a fraudulent transaction. The key contribution in this study is to focus on the profit maximization in the model building step. The artificial neural network proposed in this study works based on profit maximization instead of minimizing the error of prediction. Moreover, some studies have shown that the back propagation algorithm, similar to other gradient–based algorithms, usually gets trapped in local optima and swarm-based algorithms are more successful in this respect. In this study, we train our profit maximization ANN using the Migrating Birds optimization (MBO) which is introduced to literature recently.
dc.language.isoengen_US
dc.publisherWorld Academy of Science, Engineering and Technology
dc.relation.ispartofICADMA 2015 : 18th International Conference on Advanced Data Mining and Applications
dc.rightsrestrictedAccess
dc.titleProfit-based artificial neural network (ANN) trained by migrating birds optimization: a case study in credit card fraud detectionen_US
dc.typeConference paperen_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.volume2
dc.identifier.issue6
dc.identifier.wosWOS:000383964500004
dc.subject.keywordsNeural network
dc.subject.keywordsProfit-based neural network
dc.subject.keywordsSum of squared errors (SSE)
dc.subject.keywordsMBO
dc.subject.keywordsGradient descent
dc.identifier.scopusSCOPUS:2-s2.0-84969988953
dc.contributor.ozugradstudentZakaryazad, Ashkan
dc.contributor.ozugradstudentKibekbaev, Azamat
dc.contributor.authorMale3
dc.relation.publicationcategoryConference Paper - International - Institutional Academic Staff and Graduate Student


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