  Ensemble learning aims to improve generalization ability by using multiple
base learners. It is well-known that to construct a good ensemble, the base
learners should be accurate as well as diverse. In this paper, unlabeled data
is exploited to facilitate ensemble learning by helping augment the diversity
among the base learners. Specifically, a semi-supervised ensemble method named
UDEED is proposed. Unlike existing semi-supervised ensemble methods where
error-prone pseudo-labels are estimated for unlabeled data to enlarge the
labeled data to improve accuracy, UDEED works by maximizing accuracies of base
learners on labeled data while maximizing diversity among them on unlabeled
data. Experiments show that UDEED can effectively utilize unlabeled data for
ensemble learning and is highly competitive to well-established semi-supervised
ensemble methods.
