  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.
