BUILDING CLASSIFICATION MODELS FROM IMBALANCED FRAUD DETECTION DATA
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: |
UiTM Press
2014-10-01
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| Series: | Malaysian Journal of Computing |
| Subjects: |
| _version_ | 1797450997594849280 |
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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 |
| collection | DOAJ |
| 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 | 2024-03-09T14:47:39Z |
| format | Article |
| id | doaj.art-a49d4a7883574332b20af00688991995 |
| institution | Directory Open Access Journal |
| issn | 2600-8238 |
| language | English |
| last_indexed | 2024-03-09T14:47:39Z |
| publishDate | 2014-10-01 |
| publisher | UiTM Press |
| record_format | Article |
| series | Malaysian Journal of Computing |
| spelling | doaj.art-a49d4a7883574332b20af006889919952023-11-26T19:16:44ZengUiTM PressMalaysian Journal of Computing2600-82382014-10-01221333https://doi.org/10.24191/mjoc.v2i2.0012BUILDING CLASSIFICATION MODELS FROM IMBALANCED FRAUD DETECTION DATATerence Yong Koon Beh0Swee Chuan Tan1Hwee Theng Yeo2School of Business, SIM UniversitySchool of Business, SIM UniversitySchool of Business, SIM UniversityMany 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. imbalanced datamachine learningmodel evaluationperformances measures |
| spellingShingle | Terence Yong Koon Beh Swee Chuan Tan Hwee Theng Yeo BUILDING CLASSIFICATION MODELS FROM IMBALANCED FRAUD DETECTION DATA Malaysian Journal of Computing imbalanced data machine learning model evaluation performances measures |
| title | BUILDING CLASSIFICATION MODELS FROM IMBALANCED FRAUD DETECTION DATA |
| title_full | BUILDING CLASSIFICATION MODELS FROM IMBALANCED FRAUD DETECTION DATA |
| title_fullStr | BUILDING CLASSIFICATION MODELS FROM IMBALANCED FRAUD DETECTION DATA |
| title_full_unstemmed | BUILDING CLASSIFICATION MODELS FROM IMBALANCED FRAUD DETECTION DATA |
| title_short | BUILDING CLASSIFICATION MODELS FROM IMBALANCED FRAUD DETECTION DATA |
| title_sort | building classification models from imbalanced fraud detection data |
| topic | imbalanced data machine learning model evaluation performances measures |
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