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.

从演化和可能的非平稳数据流中进行自适应学习的问题在最近引起了机器学习的极大兴趣，也刺激了相关领域的研究，如计算智能和模糊系统。特别是，已经提出了几种基于规则的回归模型增量归纳方法。在本文中，我们开发了一种方法，该方法结合了植根于不同学习范式的两种现有方法的优势。更具体地说，我们的方法采用了最先进的学习算法 AMRules 的基本原理，并通过模糊规则的表示优势对其进行了丰富。在一项全面的实验研究中，TSK-Streams 被证明在性能方面具有很强的竞争力。