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OFAI-TR-2001-02 ( 57kB g-zipped PostScript file,  130kB PDF file)

Round Robin Rule Learning

Johannes Fürnkranz

In this paper, we discuss a technique for handling multi-class problems with binary classifiers. The idea - learning one classifier for each pair of classes - is known as pairwise classification but - to our knowledge - has not yet been thoroughly investigated in the context of inductive rule learning. We present an empirical evaluation of the method as a wrapper around the Ripper rule learning algorithm on 20 multi-class datasets from the UCI database repository. Our results show that the method is very likely to improve Ripper's classification performance without having a high risk of decreasing it. The size of this improvement is similar to that obtained by boosting C5. In addition, we give a theoretical analysis of the complexity of the approach and show that its training time is within a small constant bound of the training time of the sequential class learning technique that is currently used in Ripper.

Keywords: Rule Learning, Pairwise Classification, Class Binarization

Citation: Fürnkranz J.: Round Robin Rule Learning. In Proceedings of the 18th International Conference on Machine Learning (ICML-01). Williamstown, MA. Morgan Kaufmann, 2001.