Prediction of ordinal classes using regression trees

This paper is devoted to the problem of learning to predict ordinal (i.e., ordered discrete) classes using classification and regression trees. We sta rt with S-CART, a tree induction algorithm, and study various ways of trans forming it into a learner for ordinal classification tasks. These algorithm variants are compared on a number of benchmark data sets to verify the rel ative strengths and weaknesses of the strategies and to study the trade-off between optimal categorical classification accuracy (hit rate) and minimum distance-based error. Preliminary results indicate that this is a promisin g avenue towards algorithms that combine aspects of classification and regr ession.