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

• Main Authors: Ferdaus, Md Meftahul, Dam, Tanmoy, Alam, Sameer, Pham, Duc-Thinh
• Other Authors: Air Traffic Management Research Institute
• Format: Article
• Language: English
• Published: 2024
• Subjects: Engineering; Concept drift; Data streams
• Online Access: https://hdl.handle.net/10356/174736
• Institution: Nanyang Technological University

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 reli...