A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches.
Mikel GalarAlberto FernándezEdurne Barrenechea TartasHumberto Bustince SolaFrancisco HerreraPublished in: IEEE Trans. Syst. Man Cybern. Part C (2012)
Keyphrases
- class imbalance
- imbalanced data
- ensemble methods
- ensemble learning
- base classifiers
- binary classification problems
- class distribution
- cost sensitive
- decision stumps
- imbalanced datasets
- decision trees
- sampling methods
- random subspaces
- machine learning methods
- machine learning
- random forests
- ensemble classifier
- base learners
- decision tree ensembles
- active learning
- concept drift
- prediction accuracy
- machine learning algorithms
- learning algorithm
- generalization ability
- random forest
- minority class
- multiple classifier systems
- feature selection
- test set
- cost sensitive learning
- majority voting
- multi class
- feature extraction