Prediction of ordinal classes using regression trees
Kramer, S., Widmer, G. Pfahringer, B. & DeGroeve, M. (2000). Prediction of ordinal classes using regression tree. In Z.W. Ras & S. Ohsuga(Eds), Proceedings of 12th International Symposium, ISMIS 2000 Charlotte, NC, USA, October 11–14, 2000. (pp. 665-674). Berlin: Springer.
Permanent Research Commons link: https://hdl.handle.net/10289/1467
This paper is devoted to the problem of learning to predict ordinal (i.e., ordered discrete) classes using classification and regression trees. We start with S-CART, a tree induction algorithm, and study various ways of transforming it into a learner for ordinal classification tasks. These algorithm variants are compared on a number of benchmark data sets to verify the relative 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 promising avenue towards algorithms that combine aspects of classification and regression.