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.
Highlights
In many practical applications of machine learning and predictive modeling, data is produced incrementally in the course of time and observed in the form of a continuous, potentially unbounded stream of observations
The data sets starting with prefix BNG- are obtained from the online machine learning platform OpenML (Bischl et al 2017); these large data streams are drawn from Bayesian networks as generative models, after constructing each network from a relatively small data set (we refer to van Rijn et al (2014) for more details)
We introduced a new fuzzy rule learner for adaptive regression on data streams, called TSK-Streams
Summary
In many practical applications of machine learning and predictive modeling, data is produced incrementally in the course of time and observed in the form of a continuous, potentially unbounded stream of observations. – We give a concise overview of regression learning on data streams as well as a systematic comparison of existing methods with regard to properties such as discretization of features, splitting criteria for rules, etc. This overview helps to better understand the specificities and characteristics of approaches originating from different research fields, as well as to position our own approach. 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).
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