1Context-Adaptive Big Data Stream Mining

Abstract—Emerging stream mining applications require clas-sification of large data streams generated by single or multiple heterogeneous sources. Different classifiers can be used to produce predictions. However, in many practical scenarios the distribution over data and labels (and hence the accuracies of the classifiers) may be unknown a priori and may change in un-predictable ways over time. We consider data streams that are characterized by their context information which can be used as meta-data to choose which classifier should be used to make a specific prediction. Since the context information can be high dimensional, learning the best classifiers to make predictions using contexts suffers from the curse of dimensionality. In this paper, we propose a context-adaptive learning algorithm which learns online what is the best context, learner, and classifier to use to process a data stream. Then, we theoretically bound the regret of the proposed algorithm and show that its time order is independent of the dimension of the context space. Our numerical results illustrate that our algorithm outperforms most prior online learning algorithms, for which such online performance bounds have not been proven. Index Terms—Stream mining, context-adaptive learning, dis-tributed multi-user learning, contextual bandits. I