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OFAI-TR-2002-20 ( 212kB PDF file)

Pairwise Classification as an Ensemble Technique

Johannes Fürnkranz

In this paper we investigate the performance of pairwise (or round robin) classification, originally a technique for turning multi-class problems into two-class problems, as a general ensemble technique. In particular, we show that the use of round robin ensembles will also increase the classification performance of decision tree learners, which could directly handle multi-class problems. The performance gain is not as large as for bagging and boosting, but on the other hand round robin ensembles have a clear semantics. Furthermore, we show that the advantage of pairwise classification over direct multi-class classification and one-against-all binarization increases with the number of classes, and that round robin ensembles form an interesting alternative for problems with ordered class values.

Keywords: Class Binarization, Pairwise Classification, Round Robin Learning, Inductive Rule Learning, Ensemble Methods

Citation: Fürnkranz J.: Pairwise Classification as an Ensemble Technique. Proceedings of the 13th European Conference on Machine Learning (ECML'02), Helsinki, Finland.