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.
@article{arxiv.1911.03951,
title = {TSK-Streams: Learning TSK Fuzzy Systems on Data Streams},
author = {Ammar Shaker and Eyke Hüllermeier},
journal= {arXiv preprint arXiv:1911.03951},
year = {2019}
}