Abadifard, SoheilGheibuni, SanazBakhshi, SepehrCan, Fazlı2024-03-192024-03-192023-10-23979-840070124-5https://hdl.handle.net/11693/114984Conference Name: 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023Date of Conference: 21 October 2023through 25 October 2023Ensemble methods are commonly used in classification due to their remarkable performance. Achieving high accuracy in a data stream environment is a challenging task considering disruptive changes in the data distribution, also known as concept drift. A greater diversity of ensemble components is known to enhance predic tion accuracy in such settings. Despite the diversity of components within an ensemble, not all contribute as expected to its overall performance. This necessitates a method for selecting components that exhibit high performance and diversity. We present a novel ensemble construction and maintenance approach based on MMR (Maximal Marginal Relevance) that dynamically combines the diver sity and prediction accuracy of components during the process of structuring an ensemble. The experimental results on both four real and 11 synthetic datasets demonstrate that the proposed approach (DynED) provides a higher average mean accuracy compared to the five state-of-the-art baselines.EnglishData stream classificationConcept driftDiversity adjustmentEnsemble learningEnsemble pruningMaximal marginal relevanceConference Paper10.1145/3583780.3615266