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Boosting has been shown to improve the performance of classifiers in many situations, including when data is imbalanced. There are, however, two possible implementations of boosting, and it is unclear which should be used. Boosting by reweighting is typically used, but can only be applied to base learners which are designed to handle example weights. On the other hand, boosting by resampling can be applied to any base learner. In this work, we empirically evaluate the differences between thesedoi:10.1109/ictai.2008.59 dblp:conf/ictai/SeiffertKHN08 fatcat:t3bf235vorbezabfvowyewbtx4