A new combination strategy for OVO is proposed by transforming the aggregation problem. New instances are classified by the similarity of their outputs with respect to those of the training instances. The possibility of carrying out pruning in OVO ensembles is introduced for the first time. An exhaustive experimental study showing the existence of redundant (non-necessary) classifiers in OVO is developed.