A network of cooperative learners for data-driven stream Mining

We propose and analyze a distributed learning system to classify data captured from distributed and dynamic data streams. Our scheme consists of multiple distributed learners that are interconnected via an exogenously-determined network. Each learner observes a specific data stream, which is correlated to a common event that needs to be classified, and maintains a set of local classifiers and a weight for each local classifier. We propose a cooperative online learning scheme in which the learners exchange information through the network both to compute an aggregate prediction and to adapt the weights to the dynamic characteristics of the data streams. The information dissemination protocol is designed to minimize the time required to compute the final prediction. We determine an upper bound for the worst-case misclas-sification probability of our scheme, which depends on the misclassification probability of the best (unknown) static aggregation rule. Importantly, such bound tends to zero if the misclassification probability of the best static aggregation rule tends to zero. When applied to well-known data sets experiencing concept drifts, our scheme exhibits gains ranging from 20% to 70% with respect to state-of-the-art solutions.