A Decision-Based Dynamic Ensemble Selection Method for Concept Drift

We propose an online method for concept drift detection based on dynamic classifier ensemble selection. The proposed method generates a pool of ensembles by promoting diversity among classifier members and chooses expert ensembles according to global prequential accuracy values. Unlike current dynamic ensemble selection approaches that use only local knowledge to select the most competent ensemble for each instance, our method focuses on selection taking into account the decision space. Consequently, it is well adapted to the context of drift detection in data stream problems. The results of the experiments show that the proposed method attained the highest detection precision and the lowest number of false alarms, besides competitive classification accuracy rates, in artificial datasets representing different types of drifts. Moreover, it outperformed baselines in different real-problem datasets in terms of classification accuracy.