2016 Management and Innovation Technology International Conference (MITicon) 2016
DOI: 10.1109/miticon.2016.8025244
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Credit card fraud detection using RUS and MRN algorithms

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Cited by 19 publications
(5 citation statements)
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“…There are two major methods of adjusting the imbalance in datasets, undersampling and oversampling. In his research, he also uses the MRN algorithm for the classification problem of credit card fraud [6].…”
Section: Related Workmentioning
confidence: 99%
“…There are two major methods of adjusting the imbalance in datasets, undersampling and oversampling. In his research, he also uses the MRN algorithm for the classification problem of credit card fraud [6].…”
Section: Related Workmentioning
confidence: 99%
“…Benmakrouha et al to elucidate consumer behavior profile, transaction amount and time of shopping is thought-out as inputs and output is suspicious but the model have yet to be developed and tested with real data. Data mining techniques for fraud detection in credit card management based on customer behaviors can improve performance concerning accuracy and sensitivity [24]. 2315 A multifactor authentication system was proposed to which knowledge factor is a 4-digit pin, possession factor is NFC-enabled Smartphone and inherence factor is the face of the user [25].…”
Section: Research Methods 21 Related Workmentioning
confidence: 99%
“…Conclude that KNN algorithm extremely well in CCFD (Credit Card Fraud Detection). A similar research domain was presented by Anusorn Charleonnan [2] they proposed a new algorithm "RUSMRN algorithm" based on three classifiers which are RUS, MRN and Naïve Bayes algorithms. The author gives a basic description about these three algorithms and proposed an algorithm step by step.…”
Section: IImentioning
confidence: 98%