Ensemble Methods for Class Imbalance Learning
Xu‐Ying Liu, Zhi‐Hua Zhou · 2013
This chapter introduces ensemble learning and gives an overview of ensemble methods for class imbalance learning. In class imbalance learning (CIL), ensemble methods are broadly used to further improve the existing methods or help design brand new ones. These methods are categorized into three groups: standard ensemble methods, CIL methods that do not use ensemble methods to handle imbalanced data, and ensemble methods for CIL. The ensemble methods are roughly categorized into Bagging-style methods, such as UnderBagging, OverBagging, SMOTEBagging, and Chan; boosting-based methods, such as Synthetic minority over-sampling technique (SMOTE)Boost, RUSBoost, and DataBoost-IM; and hybrid ensemble methods, such as EasyEnsemble and BalanceCascade. Controlled Vocabulary Terms bagging; learning (artificial intelligence)