Decision trees for ordinal classification

Article type: Research Article

Authors: Potharst, Rob | Bioch, Jan C.

Affiliations: Erasmus University Rotterdam, P.O. Box 1738, 3000 DR Rotterdam, The Netherlands

Abstract: In many classification problems the domains of the attributes and the classes are linearly ordered. For such problems the classification rule often needs to be order-preserving or monotonic as we call it. Since the known decision tree methods generate non-monotonic trees, these methods are not suitable for monotonic classification problems. We provide an order-preserving tree-generation algorithm for multi-attribute classification problems with k linearly ordered classes, and an algorithm for repairing non-monotonic decision trees. The performance of these algorithms is tested on a real-world financial dataset and on random monotonic datasets.

Keywords: classification, ordinal data, decision tree

DOI: 10.3233/IDA-2000-4202

Journal: Intelligent Data Analysis, vol. 4, no. 2, pp. 97-111, 2000