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Credit card fraud detection using a hierarchical behavior-knowledge space model
journal contribution
posted on 2022-01-20, 00:00 authored by A K Nandi, K K Randhawa, H S Chua, M Seera, Chee Peng LimChee Peng LimWith the advancement in machine learning, researchers continue to devise and implement effective intelligent methods for fraud detection in the financial sector. Indeed, credit card fraud leads to billions of dollars in losses for merchants every year. In this paper, a multiclassifier framework is designed to address the challenges of credit card fraud detections. An ensemble model with multiple machine learning classification algorithms is designed, in which the Behavior-Knowledge Space (BKS) is leveraged to combine the predictions from multiple classifiers. To ascertain the effectiveness of the developed ensemble model, publicly available data sets as well as real financial records are employed for performance evaluations. Through statistical tests, the results positively indicate the effectiveness of the developed model as compared with the commonly used majority voting method for combination of predictions from multiple classifiers in tackling noisy data classification as well as credit card fraud detection problems.
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Journal
PLoS ONEVolume
17Issue
1Article number
e0260579.Pagination
1 - 10Publisher
Public Library of Science (PLoS)Location
San Francisco, Calif.Publisher DOI
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1932-6203Language
engPublication classification
C1 Refereed article in a scholarly journalUsage metrics
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