X-Fuzz: an evolving and interpretable neurofuzzy learner for data streams

Abstract

While evolving neuro-fuzzy systems have shown promise for learning from non-stationary streaming data with concept drift, most existing models lack transparency due to the limited interpretability of Takagi-Sugeno fuzzy architecture’s linear rule consequents. The lack of transparency limits the reliability of crucial applications. To address this limitation, this paper proposes a new evolving neuro-fuzzy system called X-Fuzz that enhances interpretability by integrating the LIME technique to provide local explanations and evaluates them using faithfulness and monotonicity metrics. X-Fuzz is rigorously tested on streaming datasets with diverse concept drifts via prequential analysis. Experiments demonstrate X-Fuzz’s capabilities in mining insights from large and dynamic data streams exhibiting diverse concept drifts including abrupt, gradual, recurring contextual, and cyclical drifts. In addition, for online runway exit prediction using real aviation data, X-Fuzz achieved 98.04% accuracy, significantly exceeding recent methods. With its balance of efficiency and transparency, X-Fuzz represents a promising approach for trustworthy evolving artificial intelligence that can handle complex, non-stationary data streams in critical real-world settings. We have made the X-Fuzz source code available in <uri>https://github.com/m-ferdaus/X</uri> Fuzz for reproducibility and facilitating future research.