The reason of poor performance of decision tree classifiers in minority class is that most of the classifiers employ a post-pruning method. Any node can be removed and assigned the most common class of the training instances that are sorted to the node in question. Thus, if a class is rare, decision tree algorithms often prune the tree down to a single node that classifies all instances as members of the common class leading to poor accuracy on the instances of minority class.