TSK-Streams: Learning TSK Fuzzy Systems on Data Streams
This is an incremental improvement for researchers in machine learning and fuzzy systems working on data stream regression.
The paper tackled the problem of adaptive learning from evolving data streams by combining AMRules with fuzzy rules, resulting in a method that is highly competitive in performance.
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.