Pairwise Classification as an Ensemble Technique

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.