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OFAI-TR-2001-18 ( 75kB g-zipped PostScript file,  154kB PDF file)

Round Robin Classification

Johannes Fürnkranz

In this paper, we discuss round robin classification (aka pairwise classification), a technique for handling multi-class problems with binary classifiers by learning one classifier for each pair of classes. We present an empirical evaluation of the method, implemented as a wrapper around the ripper rule learning algorithm, on 20 multi-class datasets from the UCI database repository. Our results show that it is very likely to improve ripper's classification accuracy without having a high risk of decreasing it. More importantly, we give a general theoretical analysis of the complexity of the approach and show that its training effort is below that of the commonly used one-against-all technique. These theoretical results are not restricted to rule learning but are also of interest to other communities where pairwise classification has recently received some attention. Furthermore, we investigate its properties as a general ensemble technique and show that round robin classification with C5.0 may improve C5.0's performance on multi-class problems. However, this improvement does not reach the performance increase of boosting, and a combination of boosting and round robin classification does not produce any gain over conventional boosting. Finally, we show that the performance of round robin classification can be further improved by performing multiple round comparisons, i.e., by integrating it with bagging.

Keywords: pairwise classification, inductive rule learning, multi-class problems, class binarization, ensemble techniques

Citation: Fürnkranz J.: Round Robin Classification. Journal of Machine Learning Research 2:721-747, March 2002.