Confusion entropy is a new measure for evaluating performance of classifiers. For each class in a classification problem, the CEN metric considers not only the misclassification information about how the true samples in this class have been misclassified to the other classes, but also the misclassification information about how the other samples have been misclassified to this class. In this paper we propose a novel splitting criterion named CENsplit based on CEN for learning decision trees with higher performance, especially with regard to class discrimination power of the induced trees. Experimental results on some data sets show that CENsplit criterion leads to trees with better CEN value without reducing accuracy.