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Incremental Ensemble Classifier Addressing Non-stationary Fast Data Streams.

, , and . ICDM Workshops, page 716-723. IEEE Computer Society, (2014)

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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)Drift Detection Using Stream Volatility., , , and . ECML/PKDD (1), volume 9284 of Lecture Notes in Computer Science, page 417-432. Springer, (2015)Batch-Incremental versus Instance-Incremental Learning in Dynamic and Evolving Data., , , and . IDA, volume 7619 of Lecture Notes in Computer Science, page 313-323. Springer, (2012)Efficient Online Evaluation of Big Data Stream Classifiers., , , , and . KDD, page 59-68. ACM, (2015)Streaming Multi-label Classification., , , and . WAPA, volume 17 of JMLR Proceedings, page 19-25. JMLR.org, (2011)Resource-Aware Edge-Based Stream Analytics., , , , and . IEEE Internet Comput., 26 (4): 79-88 (2022)Recurring concept meta-learning for evolving data streams., , , and . Expert Syst. Appl., (2019)River: machine learning for streaming data in Python., , , , , , , , , and 1 other author(s). CoRR, (2020)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)