TSK-Streams: learning TSK fuzzy systems for regression on data streams
The problem of adaptive learning from evolving and possibly non-stationary data streams has attracted a lot of interest in machine learning in the recent past, and also stimulated research in related fields, such as computational intelligence and fuzzy systems. In particular, several rule-based meth...
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| Veröffentlicht in: | Data mining and knowledge discovery 2021-09, Vol.35 (5), p.1941-1971 |
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| container_end_page | 1971 |
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| container_issue | 5 |
| container_start_page | 1941 |
| container_title | Data mining and knowledge discovery |
| container_volume | 35 |
| creator | Shaker, Ammar Hüllermeier, Eyke |
| description | The problem of adaptive learning from evolving and possibly non-stationary data streams has attracted a lot of interest in machine learning in the recent past, and also stimulated research in related fields, such as computational intelligence and fuzzy systems. 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. |
| doi_str_mv | 10.1007/s10618-021-00769-1 |
| format | Article |
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| subjects | Algorithms Artificial Intelligence Chemistry and Earth Sciences Computer Science Data Mining and Knowledge Discovery Data transmission Distance learning Fuzzy sets Fuzzy systems Hypotheses Information Storage and Retrieval Machine learning Methods Physics Regression models Statistics for Engineering Trees |
| title | TSK-Streams: learning TSK fuzzy systems for regression on data streams |
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