{"type":"ScholarlyArticle","title":"Recurrent Concept Drifts on Data Streams","authors":[{"name":"Nuwan Gunasekara","slug":"nuwan-gunasekara","given":"Nuwan","family":"Gunasekara","affiliation":"AI Institute , University of Waikato"},{"name":"Bernhard Pfahringer","slug":"bernhard-pfahringer","given":"Bernhard","family":"Pfahringer","affiliation":"AI Institute , University of Waikato"},{"name":"Heitor Murilo Gomes","slug":"heitor-murilo-gomes","given":"Heitor Murilo","family":"Gomes","affiliation":"Victoria University of Wellington"},{"name":"Albert Bifet","slug":"albert-bifet","given":"Albert","family":"Bifet","affiliation":"AI Institute , University of Waikato; LTCI , Télécom Paris , IP Paris"},{"name":"Yun Sing Koh","slug":"yun-sing-koh","given":"Yun Sing","family":"Koh","affiliation":"School of Computer Science , University of Auckland"}],"abstract":"In an era where machine learning permeates every facet of human existence, and data evolves incessantly, the application of machine learning models transcends mere data processing. It involves navigating constant changes exemplified by the phenomenon of concept drift, which often affects model performance.\r\nThese drifts can be recurrent due to the cyclic nature of the underlying data generation processes, which could be influenced by recurrent phenomena such as weather and time of the day. \r\nStream Learning on data streams with recurrent concept drifts attempts to learn from such streams of data.\r\nThe survey underscores the significance of the field and its practical applications, delving into nuanced definitions of machine learning for data streams afflicted by recurrent concept drifts. It explores diverse methodological approaches, elucidating their key design components. Additionally, it examines various evaluation techniques, benchmark datasets, and available software tailored for simulating and analysing data streams with recurrent concept drifts. Concluding, the survey offers insights into potential avenues for future research in the field.","doi":"10.24963/ijcai.2024/888","url":"https://ijcai.org/proceedings/2024/888","pdf":"https://ijcai.org/proceedings/2024/0888.pdf","markdown":"https://ijcai.org/proceedings/2024/888.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/888","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Survey Track","track":"Survey Track","number":888,"pages":{"first":8029,"last":8037},"keywords":["Data Mining: DM: Mining data streams","Machine Learning: ML: Time series and data streams"],"videos":[],"references":[{"position":1,"raw":"[Adams and MacKay, 2007] Ryan Prescott Adams and David JC MacKay. 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