There are several variations of such selection mechanisms (Abney, 2007), and we rely on a balancing approach that considers the same number of instances for each class.
we select a subset {x s | s = 1, ..., K t } of the unlabeled data for each class, containing the top K t highest confidence items based on the current trained model.