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EACD: evolutionary adaptation to concept drifts in data streams.

, , and . Data Min. Knowl. Discov., 33 (3): 663-694 (2019)

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EACD: evolutionary adaptation to concept drifts in data streams., , and . Data Min. Knowl. Discov., 33 (3): 663-694 (2019)Internet of Things and data mining: From applications to techniques and systems., , , , , , and . WIREs Data Mining Knowl. Discov., (2019)Feature engineering for improving robustness of crossover in symbolic regression., , , , and . GECCO Companion, page 249-250. ACM, (2020)A Graph-Based Approach to Interpreting Recurrent Neural Networks in Process Mining., , and . IEEE Access, (2020)Time Control or Size Control? Reducing Complexity and Improving Accuracy of Genetic Programming Models., , , , and . EuroGP, volume 12101 of Lecture Notes in Computer Science, page 195-210. Springer, (2020)PGraphD*: Methods for Drift Detection and Localisation Using Deep Learning Modelling of Business Processes., , and . Entropy, 24 (7): 910 (2022)RED-GENE: An Evolutionary Game Theoretic Approach to Adaptive Data Stream Classification., , and . IEEE Access, (2019)Leveraging asynchronous parallel computing to produce simple genetic programming computational models., , , , and . SAC, page 521-528. ACM, (2020)Improving the generalisation of genetic programming models with evaluation time and asynchronous parallel computing., , , , and . GECCO Companion, page 265-266. ACM, (2021)A non-canonical hybrid metaheuristic approach to adaptive data stream classification., , and . Future Gener. Comput. Syst., (2020)