Boosting for Learning From Imbalanced, Multiclass Data Sets
In many real-world applications, it is common to have uneven number of examples among multiple classes. The data imbalance, however, usually complicates the learning process, especially for the minority classes, and results in deteriorated performance. Boosting methods were proposed to handle the im...
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| Format: | Others |
| Language: | English |
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University of North Texas
2013
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| Online Access: | https://digital.library.unt.edu/ark:/67531/metadc407775/ |