Affiliations: Institute for Computer Science,
Albert-Ludwigs-University Freiburg, Georges-Köhler-Allee Geb. 79, D-79110
Freiburg i. Br., Germany (e-mail: [email protected]) | Austrian Research Institute for Artificial
Intelligence, Schotteng. 3, A-1010 Vienna, Austria (e-mail: [email protected]) | Department of Computer Science, University of Waikato
Hamilton, New Zealand (e-mail: [email protected]) | Department of Computer Science, Katholieke
Universiteit Leuven, Leuven, Belgium
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.
