Decision trees have three main disadvantages: reduced performance when the training set is small; rigid decision criteria; and the fact that a single “uncharacteristic” attribute might “derail” the classification process. In this paper we present ConfDTree(Confidence-Based Decision Tree) — a post-processing method that enables decision trees to better classify outlier instances. This method, which can be applied to any decision tree algorithm, uses easy-to-implement statistical methods(confidence intervals and two-proportion tests) in order to identify hard-to-classify instances and to propose alternative routes. The experimental study indicates that the proposed post-processing method consistently and significantly improves the predictive performance of decision trees, particularly for small, imbalanced or multi-class datasets in which an average improvement of 5%～9% in the AUC performance is reported. Decision trees have three main disadvantages: reduced performance when the training set is small; rigid decision criteria; and the fact that a single “uncharacteristic” attribute might “” derail "the classification process. In this paper we present ConfDTree (Confidence -Based Decision Tree) - a post-processing method that enables decisions trees to better classify outlier instances. This method, which can be applied to any decision tree algorithm, uses easy-to-implement statistical methods (confidence intervals and two-proportion tests ) in order to identify hard-to-classify instances and to propose alternative routes. The experimental study that that proposed post-processing method consistently and significantly improves the predictive performance of decision trees, particularly for small, imbalanced or multi-class datasets in which an average improvement of 5% ~ 9% in the AUC performance is reported.