Item

Prediction of ordinal classes using regression trees

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.
Type
Conference Contribution
Type of thesis
Series
Citation
Kramer, S., Widmer, G. Pfahringer, B. & DeGroeve, M. (2000). Prediction of ordinal classes using regression tree. In Z.W. Ras & S. Ohsuga(Eds), Proceedings of 12th International Symposium, ISMIS 2000 Charlotte, NC, USA, October 11–14, 2000. (pp. 665-674). Berlin: Springer.
Date
2000
Publisher
Springer, Berlin
Degree
Supervisors
Rights