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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)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)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)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)Streaming Time Series Forecasting using Multi-Target Regression with Dynamic Ensemble Selection., , , and . IEEE BigData, page 2170-2179. IEEE, (2020)Arbitrated Dynamic Ensemble with Abstaining for Time-Series Forecasting on Data Streams., , and . IEEE BigData, page 1040-1045. IEEE, (2019)Learning Fast and Slow: A Unified Batch/Stream Framework., , , , and . IEEE BigData, page 1065-1072. IEEE, (2018)Choosing the Right Time to Learn Evolving Data Streams., , and . IEEE Big Data, page 5156-5165. IEEE, (2023)