Why Does Rebalancing Class-Unbalanced Data Improve AUC for Linear Discriminant Analysis?
Many established classifiers fail to identify the minority class when it is much smaller than the majority class. To tackle this problem, researchers often first rebalance the class sizes in the training dataset, through oversampling the minority class or undersampling the majority class, and then u...
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 37(2015), 5 vom: 01. Mai, Seite 1109-12 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
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2015
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence |
| Schlagworte: | Journal Article Research Support, Non-U.S. Gov't |
| Online verfügbar |
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