Summary
Ensemble methods in machine learning create strong classifiers by combining results from multiple weak classifiers using approaches such as bagging and boosting. However, these methods assume balanced data and may struggle with imbalanced datasets. Combining ensemble methods with sampling methods such as oversampling and undersampling leads to techniques such as UnderBagging, OverBagging, and SMOTEBagging, all of which can help address imbalanced data issues.
Ensembles of ensembles, such as EasyEnsemble, combine boosting and bagging techniques to create powerful classifiers for imbalanced datasets.
Ensemble-based imbalance learning techniques can be an excellent addition to your toolkit. The ones based on KNN, viz., SMOTEBoost, and RAMOBoost can be slow. However, the ensembles based on random undersampling and random oversampling are less costly. Also, boosting methods are found to sometimes work better than bagging methods in the case of imbalanced data. We can combine...
