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Handling class imbalance in credit card fraud using resampling methods

Hordri, Nur Farhana and Yuhaniz, Siti Sophiayati and Mohd. Azmi, Nurulhuda Firdaus and Shamsuddin, Siti Mariyam (2018) Handling class imbalance in credit card fraud using resampling methods. International Journal of Advanced Computer Science and Applications, 9 (11). pp. 390-396. ISSN 2158-107X


Official URL: http://dx.doi.org/10.14569/ijacsa.2018.091155


Credit card based online payments has grown intensely, compelling the financial organisations to implement and continuously improve their fraud detection system. However, credit card fraud dataset is heavily imbalanced and different types of misclassification errors may have different costs and it is essential to control them, to a certain degree, to compromise those errors. Classification techniques are the promising solutions to detect the fraud and non-fraud transactions. Unfortunately, in a certain condition, classification techniques do not perform well when it comes to huge numbers of differences in minority and majority cases. Hence in this study, resampling methods, Random Under Sampling, Random Over Sampling and Synthetic Minority Oversampling Technique, were applied in the credit card dataset to overcome the rare events in the dataset. Then, the three resampled datasets were classified using classification techniques. The performances were measured by their sensitivity, specificity, accuracy, precision, area under curve (AUC) and error rate. The findings disclosed that by resampling the dataset, the models were more practicable, gave better performance and were statistically better.

Item Type:Article
Uncontrolled Keywords:misclassification error, random oversampling, random undersampling
Subjects:T Technology > T Technology (General)
Divisions:Razak School of Engineering and Advanced Technology
ID Code:86470
Deposited By: Yanti Mohd Shah
Deposited On:30 Sep 2020 16:40
Last Modified:30 Sep 2020 16:40

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