Prediction of Ordinal Classes Using Regression Trees

Authors:
Stefan Kramer;Gerhard Widmer;Bernhard Pfahringer;Michael De Groeve
Affiliations:
Institute for Computer Science, Albert-Ludwigs-University Freiburg, Georges-Köhler-Allee Geb. 79, D-79110 Freiburg i. Br., Germany (e-mail: skramer@informatik.uni-freiburg.de);Austrian Research Institute for Artificial Intelligence, Schotteng. 3, A-1010 Vienna, Austria (e-mail: gerhard@ai.univie.ac.at);Department of Computer Science, University of Waikato Hamilton, New Zealand (e-mail: bernhard@cs.waikato.ac.nz);Department of Computer Science, Katholieke Universiteit Leuven, Leuven, Belgium
Venue:
Fundamenta Informaticae - Intelligent Systems
Year:
2001

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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.