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We present and empirically evaluate an efficient algorithm that learns toaggregate the predictions of an ensemble of binary classifiers. The algorithmuses the structure of the ensemble predictions on unlabeled data to yieldsignificant performance improvements. It does this without making assumptionson the structure or origin of the ensemble, without parameters, and as scalablyas linear learning. We empirically demonstrate these performance gains withrandom forests.

Scalable Semi-Supervised Aggregation of Classifiers