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Fast Unsupervised Online Drift Detection Using Incremental Kolmogorov-Smirnov Test.

, , , and . KDD, page 1545-1554. ACM, (2016)

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Classifying and Counting with Recurrent Contexts., , , and . KDD, page 1983-1992. ACM, (2018)DyS: A Framework for Mixture Models in Quantification., , , and . AAAI, page 4552-4560. AAAI Press, (2019)Combining instance selection and self-training to improve data stream quantification., , and . J. Braz. Comp. Soc., 24 (1): 12:1-12:17 (2018)Fast Unsupervised Online Drift Detection Using Incremental Kolmogorov-Smirnov Test., , , and . KDD, page 1545-1554. ACM, (2016)Unsupervised context switch for classification tasks on data streams with recurrent concepts., , and . SAC, page 518-524. ACM, (2018)One-Class Quantification., , , and . ECML/PKDD (1), volume 11051 of Lecture Notes in Computer Science, page 273-289. Springer, (2018)The Importance of the Test Set Size in Quantification Assessment., , , and . IJCAI, page 2640-2646. ijcai.org, (2020)Scheduled for July 2020, Yokohama, Japan, postponed due to the Corona pandemic..Challenges in benchmarking stream learning algorithms with real-world data., , , and . Data Min. Knowl. Discov., 34 (6): 1805-1858 (2020)On the Need of Class Ratio Insensitive Drift Tests for Data Streams., , , and . LIDTA@ECML/PKDD, volume 94 of Proceedings of Machine Learning Research, page 110-124. PMLR, (2018)Quantification in Data Streams: Initial Results., , and . BRACIS, page 43-48. IEEE Computer Society, (2017)