Comparative analysis of different distributions dataset by using data mining techniques on credit card fraud detection

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dc.contributor.authorAta, Oğuz
dc.contributor.authorHazim, Layth
dc.date.accessioned2021-05-15T11:33:45Z
dc.date.available2021-05-15T11:33:45Z
dc.date.issued2020
dc.departmentMühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümüen_US
dc.descriptionAta, Oguz/0000-0003-4511-7694; Hazim, Layth/0000-0001-8066-2175
dc.description.abstractBanks suffer multimillion-dollars losses each year for several reasons, the most important of which is due to credit card fraud. The issue is how to cope with the challenges we face with this kind of fraud. Skewed "class imbalance" is a very important challenge that faces this kind of fraud. Therefore, in this study, we explore four data mining techniques, namely naive Bayesian (NB),Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Random Forest (RF), on actual credit card transactions from European cardholders. This paper offers four major contributions. First, we used under-sampling to balance the dataset because of the high imbalance class, implying skewed distribution. Second, we applied NB, SVM, KNN, and RF to under-sampled class to classify the transactions into fraudulent and genuine followed by testing the performance measures using a confusion matrix and comparing them. Third, we adopted cross-validation (CV) with 10 folds to test the accuracy of the four models with a standard deviation followed by comparing the results for all our models. Next, we examined these models against the entire dataset (skewed) using the confusion matrix and AUC (Area Under the ROC Curve) ranking measure to conclude the final results to determine which would be the best model for us to use with a particular type of fraud. The results showing the best accuracy for the NB, SVM, KNN and RF classifiers are 97,80%; 97,46%; 98,16% and 98,23%, respectively. The comparative results have been done by using four-division datasets (75:25), (90:10), (66:34) and (80:20) displayed that the RF performs better than NB, SVM, and KNN, and the results when utilizing our proposed models on the entire dataset (skewed), achieved preferable outcomes to the under-sampled dataset.en_US
dc.description.sponsorshipULB Machine Learning Groupen_US
dc.description.sponsorshipWe thank and wish for an increase in knowledge to Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and GianlucaBontempi in [29] for supporting by the real dataset transactions from ULB Machine Learning Group and their description of dataset.en_US
dc.identifier.doi10.17559/TV-20180427091048
dc.identifier.endpage626en_US
dc.identifier.issn1330-3651
dc.identifier.issn1848-6339
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85083436143
dc.identifier.scopusqualityQ3
dc.identifier.startpage618en_US
dc.identifier.urihttps://doi.org/10.17559/TV-20180427091048
dc.identifier.urihttps://hdl.handle.net/20.500.12939/221
dc.identifier.volume27en_US
dc.identifier.wosWOS:000527241900037
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorAta, Oğuz
dc.language.isoen
dc.publisherUniv Osijek, Tech Facen_US
dc.relation.ispartofTehnicki Vjesnik-Technical Gazette
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCredit Card Fraud Detectionen_US
dc.subjectData Miningen_US
dc.subjectK-Nearest Neighbouren_US
dc.subjectNaive Bayesianen_US
dc.subjectRandom Foresten_US
dc.subjectSupport Vector Machineen_US
dc.titleComparative analysis of different distributions dataset by using data mining techniques on credit card fraud detection
dc.typeArticle

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