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Classifiers have difficulty recognizing under-represented minorities in imbalanced datasets, due to their focus on minimizing the overall misclassification error. This introduces predictive biases against minority classes. Post-processing plug-in rules are popular for tackling class imbalance, but they often affect the certainty of base classifier posteriors, when the latter already perform correct classification. This shortcoming makes them ill-suited to scoring tasks, where informativedblp:conf/pkdd/KrasanakisXPK17 fatcat:a7kpwztvt5dotimcivh7i2p6uq