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: |