Handling class imbalance in credit card fraud using resampling methods
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 i...
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Online Access: | http://eprints.utm.my/id/eprint/86470/1/NurFarhanaHordri2018_HandlingClassImbalanceinCreditCard.pdf http://eprints.utm.my/id/eprint/86470/ http://dx.doi.org/10.14569/ijacsa.2018.091155 |
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my.utm.864702020-09-30T08:40:55Z http://eprints.utm.my/id/eprint/86470/ Handling class imbalance in credit card fraud using resampling methods Hordri, Nur Farhana Yuhaniz, Siti Sophiayati Mohd. Azmi, Nurulhuda Firdaus Shamsuddin, Siti Mariyam T Technology (General) 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. Science and Information Organization 2018 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/86470/1/NurFarhanaHordri2018_HandlingClassImbalanceinCreditCard.pdf 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 http://dx.doi.org/10.14569/ijacsa.2018.091155 DOI:10.14569/ijacsa.2018.091155 |
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T Technology (General) Hordri, Nur Farhana Yuhaniz, Siti Sophiayati Mohd. Azmi, Nurulhuda Firdaus Shamsuddin, Siti Mariyam Handling class imbalance in credit card fraud using resampling methods |
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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. |
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Article |
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Hordri, Nur Farhana Yuhaniz, Siti Sophiayati Mohd. Azmi, Nurulhuda Firdaus Shamsuddin, Siti Mariyam |
author_facet |
Hordri, Nur Farhana Yuhaniz, Siti Sophiayati Mohd. Azmi, Nurulhuda Firdaus Shamsuddin, Siti Mariyam |
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Hordri, Nur Farhana |
title |
Handling class imbalance in credit card fraud using resampling methods |
title_short |
Handling class imbalance in credit card fraud using resampling methods |
title_full |
Handling class imbalance in credit card fraud using resampling methods |
title_fullStr |
Handling class imbalance in credit card fraud using resampling methods |
title_full_unstemmed |
Handling class imbalance in credit card fraud using resampling methods |
title_sort |
handling class imbalance in credit card fraud using resampling methods |
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Science and Information Organization |
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2018 |
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http://eprints.utm.my/id/eprint/86470/1/NurFarhanaHordri2018_HandlingClassImbalanceinCreditCard.pdf http://eprints.utm.my/id/eprint/86470/ http://dx.doi.org/10.14569/ijacsa.2018.091155 |
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