Building classification models from imbalanced fraud detection data / Terence Yong Koon Beh, Swee Chuan Tan and Hwee Theng Yeo
Many real-world data sets exhibit imbalanced class distributions in which almost all instances are assigned to one class and far fewer instances to a smaller, yet usually interesting class. Building classification models from such imbalanced data sets is a relatively new challenge in the machine lea...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit UiTM
2014
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| Online Access: | https://ir.uitm.edu.my/id/eprint/13930/ |
| _version_ | 1848803348366491648 |
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| author | Terence, Yong Koon Beh Swee, Chuan Tan Hwee, Theng Yeo |
| author_facet | Terence, Yong Koon Beh Swee, Chuan Tan Hwee, Theng Yeo |
| author_sort | Terence, Yong Koon Beh |
| building | UiTM Institutional Repository |
| collection | Online Access |
| description | Many real-world data sets exhibit imbalanced class distributions in which almost all instances are assigned to one class and far fewer instances to a smaller, yet usually interesting class. Building classification models from such imbalanced data sets is a relatively new challenge in the machine learning and data mining community because many traditional classification algorithms assume similar proportions of majority and minority classes. When the data is imbalanced, these algorithms generate models that achieve good classification accuracy for the majority class, but poor accuracy for the minority class. This paper reports our experience in applying data balancing techniques to develop a classifier for an imbalanced real-world fraud detection data set. We evaluated the models generated from seven classification algorithms with two simple data balancing techniques. Despite many ideas floating in the literature to tackle the imbalanced issue, our study shows the simplest data balancing technique is all that is required to significantly improve the accuracy in identifying the primary class of interest (i.e., the minority class) in all the seven algorithms tested. Our results also show that precision and recall are useful and effective measures for evaluating models created from artificially balanced data. Hence, we advise data mining practitioners to try simple data balancing first before exploring more sophisticated techniques to tackle the class imbalance problem. |
| first_indexed | 2025-11-14T21:37:49Z |
| format | Article |
| id | uitm-13930 |
| institution | Universiti Teknologi MARA |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T21:37:49Z |
| publishDate | 2014 |
| publisher | Penerbit UiTM |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uitm-139302022-06-14T02:45:50Z https://ir.uitm.edu.my/id/eprint/13930/ Building classification models from imbalanced fraud detection data / Terence Yong Koon Beh, Swee Chuan Tan and Hwee Theng Yeo mjoc Terence, Yong Koon Beh Swee, Chuan Tan Hwee, Theng Yeo Many real-world data sets exhibit imbalanced class distributions in which almost all instances are assigned to one class and far fewer instances to a smaller, yet usually interesting class. Building classification models from such imbalanced data sets is a relatively new challenge in the machine learning and data mining community because many traditional classification algorithms assume similar proportions of majority and minority classes. When the data is imbalanced, these algorithms generate models that achieve good classification accuracy for the majority class, but poor accuracy for the minority class. This paper reports our experience in applying data balancing techniques to develop a classifier for an imbalanced real-world fraud detection data set. We evaluated the models generated from seven classification algorithms with two simple data balancing techniques. Despite many ideas floating in the literature to tackle the imbalanced issue, our study shows the simplest data balancing technique is all that is required to significantly improve the accuracy in identifying the primary class of interest (i.e., the minority class) in all the seven algorithms tested. Our results also show that precision and recall are useful and effective measures for evaluating models created from artificially balanced data. Hence, we advise data mining practitioners to try simple data balancing first before exploring more sophisticated techniques to tackle the class imbalance problem. Penerbit UiTM 2014 Article PeerReviewed text en https://ir.uitm.edu.my/id/eprint/13930/1/13930.pdf Terence, Yong Koon Beh and Swee, Chuan Tan and Hwee, Theng Yeo (2014) Building classification models from imbalanced fraud detection data / Terence Yong Koon Beh, Swee Chuan Tan and Hwee Theng Yeo. (2014) Malaysian Journal of Computing (MJoC) <https://ir.uitm.edu.my/view/publication/Malaysian_Journal_of_Computing_=28MJoC=29.html>, 2 (2). pp. 13-33. ISSN 2231-7473 https://mjoc.uitm.edu.my/ |
| spellingShingle | Terence, Yong Koon Beh Swee, Chuan Tan Hwee, Theng Yeo Building classification models from imbalanced fraud detection data / Terence Yong Koon Beh, Swee Chuan Tan and Hwee Theng Yeo |
| title | Building classification models from imbalanced fraud detection data / Terence Yong Koon Beh, Swee Chuan Tan and Hwee Theng Yeo |
| title_full | Building classification models from imbalanced fraud detection data / Terence Yong Koon Beh, Swee Chuan Tan and Hwee Theng Yeo |
| title_fullStr | Building classification models from imbalanced fraud detection data / Terence Yong Koon Beh, Swee Chuan Tan and Hwee Theng Yeo |
| title_full_unstemmed | Building classification models from imbalanced fraud detection data / Terence Yong Koon Beh, Swee Chuan Tan and Hwee Theng Yeo |
| title_short | Building classification models from imbalanced fraud detection data / Terence Yong Koon Beh, Swee Chuan Tan and Hwee Theng Yeo |
| title_sort | building classification models from imbalanced fraud detection data / terence yong koon beh, swee chuan tan and hwee theng yeo |
| url | https://ir.uitm.edu.my/id/eprint/13930/ https://ir.uitm.edu.my/id/eprint/13930/ |