Boosting for Learning From Imbalanced, Multiclass Data Sets
Description:
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 imbalance problem. These methods need elongated training time and require diversity among the classifiers of the ensemble to achieve improved performance. Additionally, extending the boosting method to…
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Date:
December 2013
Creator:
Abouelenien, Mohamed
Partner:
UNT Libraries