  Extracting actionable intelligence from distributed, heterogeneous,
correlated and high-dimensional data sources requires run-time processing and
learning both locally and globally. In the last decade, a large number of
meta-learning techniques have been proposed in which local learners make online
predictions based on their locally-collected data instances, and feed these
predictions to an ensemble learner, which fuses them and issues a global
prediction. However, most of these works do not provide performance guarantees
or, when they do, these guarantees are asymptotic. None of these existing works
provide confidence estimates about the issued predictions or rate of learning
guarantees for the ensemble learner. In this paper, we provide a systematic
ensemble learning method called Hedged Bandits, which comes with both long run
(asymptotic) and short run (rate of learning) performance guarantees. Moreover,
our approach yields performance guarantees with respect to the optimal local
prediction strategy, and is also able to adapt its predictions in a data-driven
manner. We illustrate the performance of Hedged Bandits in the context of
medical informatics and show that it outperforms numerous online and offline
ensemble learning methods.
