Learning TSK Fuzzy Rules from Data Streams

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.

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

“…A popular approach for learning on data streams, both for classification and regression, is rule induction, in the fuzzy logic and computational intelligence community also known as "evolving fuzzy systems" (Lughofer 2011). Shaker et al (2017) proposed a method for regression that builds on a very efficient and effective technique for rule induction, which is inspired by the state-of-the-art machine learning algorithm AMRules (Almeida et al 2013), and combines it with the strengths of fuzzy modeling. Thus, the method induces a set of fuzzy rules, which, compared to conventional rules with Boolean antecedents, has the advantage of producing smooth regression functions.…”

“…The method presented in this paper, called TSK-Streams, is a substantially revised and improved variant of (Shaker et al 2017). The main modifications and novel contributions are as follows:…”

“…-We introduce a new strategy for the induction of TSK fuzzy rules and realize it in the form of two concrete variants: variance reduction and error reduction. While the former is still close to Shaker et al (2017), the variance reduction approach has not been considered for online learning of fuzzy systems so far. Compared with error reduction and other state-of-the-art methods, it leads to models with superior predictive performance.…”

“…Compared with error reduction and other state-of-the-art methods, it leads to models with superior predictive performance. -In Shaker et al (2017), rule antecedents may contain disjunctions and negations, which makes them difficult to understand and interpret. The representation of TSK rules used in this paper is simpler and more concise.…”

“…-We propose the induction of candidate fuzzy rules using a discretization technique that is based on an extended Binary Search Tree (E-BST) structure. Compared to the three-layered discretization architecture used by Shaker et al (2017), the use of E-BST for constructing candidate fuzzy sets has a number of advantages in the context of online learning. Most notably, it comes with a reduction of complexity from linear to logarithmic (in the number of candidate extensions).…”