Prediction of Ordinal Classes Using Regression Trees

Authors:
Stefan Kramer;Gerhard Widmer;Bernhard Pfahringer;Michael de Groeve
Affiliations:
-;-;-;-
Venue:
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
Year:
2000

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Abstract

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.