Monotonic decision tree for ordinal classification

While ordinal classification problems are common in many situations, induction of ordinal decision trees has not been very extensiveness studied. They are commonly treated as nominal classification problem or regression problem in tree induction. On the other hand a monotonic decision tree is often desirable to aid decision making in such situations as credit rating and student admission. This paper proposes a novel approach called MDT to monotonic decision tree induction. Experiments show that generally this new approach produces decision trees that are more succinct and more effective predictors of the original implicit ordering, apart from being monotonic.