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.
CITATION STYLE
Kramer, S., Widmer, G., Pfahringer, B., & De Groeve, M. (2001). Prediction of ordinal classes using regression trees. Fundamenta Informaticae, 47(1–2), 1–13. https://doi.org/10.1007/3-540-39963-1_45
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