  We present an efficient distributed online learning scheme to classify data
captured from distributed, heterogeneous, and dynamic data sources. Our scheme
consists of multiple distributed local learners, that analyze different streams
of data that are correlated to a common event that needs to be classified. Each
learner uses a local classifier to make a local prediction. The local
predictions are then collected by each learner and combined using a weighted
majority rule to output the final prediction. We propose a novel online
ensemble learning algorithm to update the aggregation rule in order to adapt to
the underlying data dynamics. We rigorously determine a bound for the worst
case misclassification probability of our algorithm which depends on the
misclassification probabilities of the best static aggregation rule, and of the
best local classifier. Importantly, the worst case misclassification
probability of our algorithm tends asymptotically to 0 if the misclassification
probability of the best static aggregation rule or the misclassification
probability of the best local classifier tend to 0. Then we extend our
algorithm to address challenges specific to the distributed implementation and
we prove new bounds that apply to these settings. Finally, we test our scheme
by performing an evaluation study on several data sets. When applied to data
sets widely used by the literature dealing with dynamic data streams and
concept drift, our scheme exhibits performance gains ranging from 34% to 71%
with respect to state of the art solutions.

Machine Learning