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: | Terence Yong Koon Beh, Swee Chuan Tan, Hwee Theng Yeo |
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Format: | Article |
Language: | English |
Published: |
UiTM Press
2014-10-01
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Series: | Malaysian Journal of Computing |
Subjects: |
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