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We propose an online method for concept driftdetection based on dynamicclassifier ensemble selection. Theproposed method generates a pool of ensemblesby promotingdiversity among classifier members and chooses expertensemblesaccording to global prequential accuracy values. Unlike currentdynamicensemble selection approaches that use only local knowl-edge to select the mostcompetent ensemble for each instance,our method focuses on selection takinginto account the decisionspace. Consequently, it is well adapted to the contextof driftdetection in data stream problems. The results of the experimentsshowthat the proposed method attained the highest detection pre-cision and thelowest number of false alarms, besides competitiveclassification accuracyrates, in artificial datasets representingdifferent types of drifts. Moreover,it outperformed baselines indifferent real-problem datasets in terms ofclassification accuracy.

A Decision-Based Dynamic Ensemble Selection Method for Concept Drift