Decision trees for ordinal classification

Abstract

In many classification problems the domains of the attributes and the classes are linearly ordered. For such problems the classification rule often needs to be order-preserving or monotonic as we call it. Since the known decision tree methods generate non-monotonic trees, these methods are not suitable for monotonic classification problems. We provide an order-preserving tree-generation algorithm for multi-attribute classification problems with k linearly ordered classes, and an algorithm for repairing non-monotonic decision trees. The performance of these algorithms is tested on a real-world financial dataset and on random monotonic datasets.