Cascade Generalization with Reweighting Data for Handling Imbalanced Problems
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.
imbalanced data sets supervised machine learning ensembles of classifiers