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Adaptive Ensembles for Evolving Data Streams - Combining Block-Based and Online Solutions.. NFMCP, volume 9607 of Lecture Notes in Computer Science, page 3-16. Springer, (2015)On the Dynamics of Classification Measures for Imbalanced and Streaming Data., , , and . IEEE Trans. Neural Networks Learn. Syst., 31 (8): 2868-2878 (2020)Analysis of Diagnostic Symptoms in Vibroacoustic Diagnostics by Means of the Rough Sets Theory., , and . Intelligent Decision Support, volume 11 of Theory and Decision Library, Springer, (1992)BRACID: a comprehensive approach to learning rules from imbalanced data., and . J. Intell. Inf. Syst., 39 (2): 335-373 (2012)Rough-Set Reasoning about Uncertain Data., and . Fundam. Informaticae, 27 (2/3): 229-243 (1996)Multi-class and feature selection extensions of Roughly Balanced Bagging for imbalanced data., and . J. Intell. Inf. Syst., 50 (1): 97-127 (2018)Feature subset selection for classification of histological images., and . Artif. Intell. Medicine, 9 (3): 227-239 (1997)Handling Continuous Attributes in Discovery of Strong Decision Rules.. Rough Sets and Current Trends in Computing, volume 1424 of Lecture Notes in Computer Science, page 394-401. Springer, (1998)The Impact of Local Data Characteristics on Learning from Imbalanced Data.. RSEISP, volume 8537 of Lecture Notes in Computer Science, page 1-13. Springer, (2014)Selective Pre-processing of Imbalanced Data for Improving Classification Performance., and . DaWaK, volume 5182 of Lecture Notes in Computer Science, page 283-292. Springer, (2008)