Local reweight wrapper for the problem of imbalance
by S.B. Kotsiantis
International Journal of Artificial Intelligence and Soft Computing (IJAISC), Vol. 1, No. 1, 2008

Abstract: Many real-world data sets exhibit skewed class distributions in which the classes are not approximately equally represented. Traditional machine learning algorithms can be biased towards majority class due to over-prevalence. This paper firstly provides a systematic study of the various methodologies that have tried to handle this problem. Finally, it presents an experimental study of these methodologies with a proposed local wrapper for reweighting training instances and it concludes that such a framework can be a more effective solution to the problem. Our method seems to allow improved identification of difficult small classes in predictive analysis, while keeping the classification ability of the other classes at an acceptable level.

Online publication date: Fri, 14-Nov-2008

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