Ensemble of distributed learners for online classification of dynamic data streams

We present a distributed online learning scheme to classify data captured from distributed and dynamic data sources. Our scheme consists of multiple distributed local learners, which 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 an upper bound for the worst-case mis-classification probability of our algorithm, which tends asymptotically to 0 if the misclassification probability of the best (unknown) static aggregation rule is 0. Then we extend our algorithm to address challenges specific to the distributed implementation and prove new bounds that apply to these settings. Finally, we test our scheme by performing an evaluation study on several data sets.