A wrapper for reweighting training instances for handling imbalanced data sets

Abstract

A classifier induced from an imbalanced data set has a low error rate for the majority class and an undesirable error rate for the minority class. This paper firstly provides a systematic study on the various methodologies that have tried to handle this problem. Finally, it presents an experimental study of these methodologies with a proposed wrapper for reweighting training instances and it concludes that such a framework can be a more valuable solution to the problem. © 2007 International Federation for Information Processing.