Round Robin Rule Learning

In this paper, we discuss a technique for handling multi-class problems with binary classifiers, namely to learn one classifier for each pair of classes. Although this idea is known in the literature, it 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. In addition, we give a theoretical analysis of the complexity of the approach and show that its training time is within a small constant factor of the training time of the sequential class binarization technique that is currently used in Ripper.