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Ensemble methods are commonly used in classification due to their remarkableperformance. Achieving high accuracy in a data stream environment is achallenging task considering disruptive changes in the data distribution, alsoknown as concept drift. A greater diversity of ensemble components is known toenhance prediction accuracy in such settings. Despite the diversity ofcomponents within an ensemble, not all contribute as expected to its overallperformance. This necessitates a method for selecting components that exhibithigh performance and diversity. We present a novel ensemble construction andmaintenance approach based on MMR (Maximal Marginal Relevance) that dynamicallycombines the diversity and prediction accuracy of components during the processof structuring an ensemble. The experimental results on both four real and 11synthetic datasets demonstrate that the proposed approach (DynED) provides ahigher average mean accuracy compared to the five state-of-the-art baselines.

DynED: Dynamic Ensemble Diversification in Data Stream Classification