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.