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Mining Frequent Closed Unordered Trees Through Natural Representations, , and . Proceedings of the 15th International Conference on Conceptual Structures (ICCS 2007), volume 4604 of Lecture Notes in Artificial Intelligence, page 347-359. Berlin, Heidelberg, Springer-Verlag, (July 2007)New ensemble methods for evolving data streams, , , , and . KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, page 139--148. New York, NY, USA, ACM, (2009)Preface., , , and . BigMine, volume 41 of JMLR Workshop and Conference Proceedings, page 4. JMLR.org, (2015)Recurring concept meta-learning for evolving data streams., , , and . Expert Syst. Appl., (2019)Resource-Aware Edge-Based Stream Analytics., , , , and . IEEE Internet Comput., 26 (4): 79-88 (2022)River: machine learning for streaming data in Python., , , , , , , , , and 1 other author(s). CoRR, (2020)Analyzing and Repairing Concept Drift Adaptation in Data Stream Classification., , , , , , , and . DSAA, page 1-2. IEEE, (2021)Incremental Rebalancing Learning on Evolving Data Streams., , and . ICDM (Workshops), page 844-850. IEEE, (2020)On Dynamic Feature Weighting for Feature Drifting Data Streams., , , , and . ECML/PKDD (2), volume 9852 of Lecture Notes in Computer Science, page 129-144. Springer, (2016)GNUsmail: Open Framework for On-line Email Classification., , , , and . ECAI, volume 215 of Frontiers in Artificial Intelligence and Applications, page 1141-1142. IOS Press, (2010)