Reviving Threshold-Moving: a Simple Plug-in Bagging Ensemble for Binary and Multiclass Imbalanced Data

Understand

Class imbalance presents a major hurdle in the application of data mining methods.

• A common practice to deal with it is to create ensembles of classifiers that learn from resampled balanced data.
• For example, bagged decision trees combined with random undersampling (RUS) or the synthetic minority oversampling technique (SMOTE).
• However, most of the resampling methods entail asymmetric changes to the examples of different classes, which in turn can introduce its own biases in the model.