TSK-Streams: learning TSK fuzzy systems for regression on data streams

Shaker, Ammar und Hüllermeier, Eyke (2021): TSK-Streams: learning TSK fuzzy systems for regression on data streams. In: Data Mining and Knowledge Discovery, Bd. 35, Nr. 5: S. 1941-1971

Abstract

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.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Mathematik, Informatik und Statistik > Informatik
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 004 Informatik
• ISSN: 1384-5810
• Sprache: Englisch