X-Fuzz: An Evolving and Interpretable Neuro-Fuzzy Learner for Data Streams

While evolving, neuro-fuzzy systems have shown promise for learning from nonstationary streaming data with concept drift, most existing models lack transparency due to the limited interpretability of Takagi–Sugeno (TS) fuzzy architecture's linear rule consequents. The lack of transparency limits the reliability of crucial applications. To address this limitation, this article proposes a new evolving neuro-fuzzy system (ENFS) called X-Fuzz that enhances interpretability by integrating the local interpretable model-agnostic explanations (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 (AI) that can handle complex, nonstationary data streams in critical real-world settings. We have made the X-Fuzz source code available at
https://github.com/m-ferdaus/X_Fuzz
for reproducibility and to facilitate future research.