  In unsupervised ensemble learning, one obtains predictions from multiple
sources or classifiers, yet without knowing the reliability and expertise of
each source, and with no labeled data to assess it. The task is to combine
these possibly conflicting predictions into an accurate meta-learner. Most
works to date assumed perfect diversity between the different sources, a
property known as conditional independence. In realistic scenarios, however,
this assumption is often violated, and ensemble learners based on it can be
severely sub-optimal. The key challenges we address in this paper are:\ (i) how
to detect, in an unsupervised manner, strong violations of conditional
independence; and (ii) construct a suitable meta-learner. To this end we
introduce a statistical model that allows for dependencies between classifiers.
Our main contributions are the development of novel unsupervised methods to
detect strongly dependent classifiers, better estimate their accuracies, and
construct an improved meta-learner. Using both artificial and real datasets, we
showcase the importance of taking classifier dependencies into account and the
competitive performance of our approach.
