Two stage partial classification for inconsistent and imbalanced classes

When deriving classification rules for a non-symmetric database with a binary target class, it is common practice to generate rules for the majority class, then any object which is not covered by a rule of suitable accuracy is by default given the minority class prediction. However, in the case where misclassification costs for the minority class significantly outweigh those of the majority class, this may mean that there are still costly incorrect predictions. We examine the capability of an evolutionary algorithm to detect these potentially costly misclassifications.