Stacking Cost Sensitive Models

A classifier induced from an imbalanced data set has, typically, a low error rate for the majority class and an unacceptable 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 stacking cost-sensitive ensemble and it concludes that such a framework can be a more effective solution to the problem.