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In particular, several rule-based methods for the incremental induction of regression models have been proposed. In this paper, we develop a method that combines the strengths of two existing approaches rooted in different learning paradigms. More concretely, our method adopts basic principles of the state-of-the-art learning algorithm AMRules and enriches them by the representational advantages of fuzzy rules. In a comprehensive experimental study, TSK-Streams is shown to be highly competitive in terms of performance.<\/jats:p>","DOI":"10.1007\/s10618-021-00769-1","type":"journal-article","created":{"date-parts":[[2021,6,23]],"date-time":"2021-06-23T08:02:58Z","timestamp":1624435378000},"page":"1941-1971","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["TSK-Streams: learning TSK fuzzy systems for regression on data streams"],"prefix":"10.1007","volume":"35","author":[{"given":"Ammar","family":"Shaker","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9944-4108","authenticated-orcid":false,"given":"Eyke","family":"H\u00fcllermeier","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,6,23]]},"reference":[{"key":"769_CR1","doi-asserted-by":"crossref","unstructured":"Almeida E, Ferreira CA, Gama J (2013) Adaptive model rules from data streams. 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