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Imbalanced learning; foundations, algorithms, and applications.


Imbalanced learning; foundations, algorithms, and applications.

Ed. by Haibo He and Yunqian Ma.



210 pages




When presented with imbalanced data sets, most standard learning and mining algorithms fail to properly represent the distributive characteristics of the data, and as a result, provide unfavorable accuracies across the classes of data. Computer scientists here review recent research into imbalanced learning, the current technologies, and critical application domains. The topics include ensemble methods for class imbalance learning, class imbalance learning methods for support vector machines, class imbalance and active learning, non-stationary stream data learning with imbalanced class distribution, and assessment metrics for imbalanced learning.

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Publication:Reference & Research Book News
Article Type:Book review
Date:Oct 1, 2013
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