Undersampling bagging ensembles specialized for class imbalanced data are considered. Particular attention is paid to Roughly Balanced Bagging, as it leads to better classification performance than other extensions of bagging. We experimentally analyze its properties with respect to bootstrap construction, deciding on the number of component classifiers, their diversity, and ability to deal with the most difficult types of the minority examples. We also discuss further extensions of under-sampling bagging, where the data difficulty factors influence sampling examples into bootstraps.
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