A Multiple Classifiers System for Anomaly Detection in Credit Card Data With Unbalanced and Overlapped Classes


Kalid, Suraya Nurain and Ng, Keng Hoong and Tong, Gee Kok and Khor, Kok Chin (2020) A Multiple Classifiers System for Anomaly Detection in Credit Card Data With Unbalanced and Overlapped Classes. IEEE Access, 8. pp. 28210-28211. ISSN 2169-3536

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Frauds and default payments are two major anomalies in credit card transactions. Researchers have been vigorously finding solutions to tackle them and one of the solutions is to use data mining approaches. However, the collected credit card data can be quite a challenge for researchers. This is because of the data characteristics that contain: (i) unbalanced class distribution, and (ii) overlapping of class samples. Both characteristics generally cause low detection rates for the anomalies that are minorities in the data. On top of that, the weakness of general learning algorithms contributes to the difficulties of classifying the anomalies as the algorithms generally bias towards the majority class samples. In this study, we used a Multiple Classifiers System (MCS) on these two data sets: (i) credit card frauds (CCF), and (ii) credit card default payments (CCDP). The MCS employs a sequential decision combination strategy to produce accurate anomaly detection. Our empirical studies show that the MCS outperforms the existing research, particularly in detecting the anomalies that are minorities in these two credit card data sets.

Item Type: Article
Uncontrolled Keywords: Computer security, Credit cards, Support vector machines, Anomaly detection, Classification algorithms, Probability, Banking
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Computing and Informatics (FCI)
Depositing User: Ms Suzilawati Abu Samah
Date Deposited: 28 Dec 2020 17:21
Last Modified: 28 Dec 2020 17:21
URII: http://shdl.mmu.edu.my/id/eprint/8027


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