Coupling different methods for overcoming the class imbalance problem

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Although the majority of concept-learning systems previously designed usually assume that their training sets are well-balanced, this assumption is not necessarily correct. Indeed, there exist many domains for which one class is represented by a large number of examples while the other is represented by only a few. The purpose of this paper is 1) to demonstrate experimentally that, at least in ...

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Learning in imbalanced datasets is a pervasive problem prevalent in a wide variety of real-world applications. In imbalanced datasets, the class of interest is generally a small fraction of the total instances, but misclassification of such instances is often expensive. While there is a significant body of research on the class imbalance problem for binary class datasets, multi-class datasets h...

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ژورنال

عنوان ژورنال: Neurocomputing

سال: 2015

ISSN: 0925-2312

DOI: 10.1016/j.neucom.2015.01.068