By training a set of classifiers on a single training corpus and then combining their outputs in classification, it is often possible to achieve a target accuracy with less labeled training data than would be needed if only one cla ssifier was being used.
as can be seen, the greater the classifier agreement, the more likely it is that a test sample has been correctly labeled.