Ensemble Methods for Class Imbalance Learning

Abstract

This chapter introduces ensemble learning and provides an overview of ensemble methods for class imbalance learning (CIL). Ensemble methods use a set of classifiers to make predictions. The generalization ability of an ensemble is usually much stronger than that of the individual ensemble members. In CIL, ensemble methods are broadly used to further improve the existing methods or help design brand new ones. According to how the base learners are generated, ensemble methods can be roughly categorized into two paradigms: parallel ensemble methods and sequential ensemble methods. According to what ensemble method is involved, ensemble methods for CIL can be roughly categorized into Bagging-style methods, boosting-based methods, hybrid ensemble methods, and other methods. Most of the ensemble methods for CIL deal with binary class problems. How to use ensemble learning to help multiclass problems is an interesting direction.