Multi-class and feature selection extensions of Roughly Balanced Bagging for imbalanced data

Roughly Balanced Bagging is one of the most efficient ensembles specialized for class imbalanced data. In this paper, we study its basic properties that may influence its good classification performance. We experimentally analyze them 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. Then, we introduce two generalizations of this ensemble for dealing with a higher number of attributes and for adapting it to handle multiple minority classes. Experiments with synthetic and real life data confirm usefulness of both proposals.