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Cascade Generalization with Reweighting Data for Handling Imbalanced Problems (2011)

Kotsiantis, S. B.

Oxford University Press

In: Computer Journal

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Publication Date: 2011-11-24

Description: Many data sets exhibit skewed class distributions in which most cases are allocated to a class and far fewer cases to a smaller one. A classifier induced from an imbalanced data set has usually a low error rate for the majority class and an unacceptable error rate for the minority class. This paper provides a review on various methodologies that have tried to handle this problem. Afterwards, it presents an experimental study of these methodologies with a proposed cascade generalization ensemble that is applied in reweighted data and it concludes that such a framework can be a more effective solution to the problem. Our method improves the identification of a difficult small class, while keeping the classification ability of the other class in an acceptable level.

Print ISSN: 0010-4620

Electronic ISSN: 1460-2067

Topics: Computer Science

Published by Oxford University Press

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