A majority voting classifier with probabilistic guarantees

This paper deals with supervised learning for classification. A new general purpose classifier is proposed that builds upon the Guaranteed Error Machine (GEM). Standard GEM can be tuned to guarantee a desired (small) misclassification probability and this is achieved by letting the classifier return an unknown label. In the proposed classifier, the size of the unknown classification region is reduced by introducing a majority voting mechanism over multiple GEMs. At the same time, the possibility of tuning the misclassification probability is retained. The effectiveness of the proposed majority voting classifier is shown on both synthetic and real benchmark data-sets, and the results are compared with other well-established classification algorithms.