iFuzz-Meta: An Interpretable Fuzzy Learning Framework Bridging Top-Down and Bottom-Up Knowledge Integration

[2608.14646] iFuzz-Meta: An Interpretable Fuzzy Learning Framework Bridging Top-Down and Bottom-Up Knowledge Integration Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2608.14646 (cs) [Submitted on 30 Jul 2026] Title: iFuzz-Meta: An Interpretable Fuzzy Learning Framework Bridging Top-Down and Bottom-Up Knowledge Integration Authors: Xiaowei Jiang , Daniel Leong , Beining Cao , Nan Zhou , Yingtao Ren , Yu-Cheng Chang , Thomas Do , Chin-Teng Lin View a PDF of the paper titled iFuzz-Meta: An Interpretable Fuzzy Learning Framework Bridging Top-Down and Bottom-Up Knowledge Integration, by Xiaowei Jiang and 7 other authors View PDF HTML (experimental) Abstract: Interpretable representation learning remains a key challenge in modern neural computation, particularly when models are expected not only to perform but also to explain their reasoning. This paper introduces iFuzz-Meta, an interpretable fuzzy rule-based learning framework that preserves human-understandable reasoning structures within modern neural architectures. Each fuzzy rule corresponds to a semantic and spatial prototype defined in the original feature space, enabling transparent inference and direct interpretability. Meta-learning is employed as an analytical paradigm to examine how these interpretable rules reorganize across tasks and domains, providing a principled means to link algorithmic adaptation with cognitive representation. A knowledge-guided regularization mechanism further enables a top-down-bottom-up integration, in which theoretical priors act as soft inductive biases while data-driven learning refines and extends them. This dual process ensures that adaptation proceeds along semantically and physiologically meaningful trajectories, rather than arbitrary parameter shifts. Evaluations demonstrate that iFuzz-Meta achieves interpretable reasoning and stable cross-domain generalization, establishing a potential general pathway toward explainable and knowledge-aware fuzzy systems. Subjects: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC) Cite as: arXiv:2608.14646 [cs.LG] (or arXiv:2608.14646v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.14646 Focus to learn more arXiv-issued DOI via DataCite Journal reference: IEEE Trans. Fuzzy Syst. 34(6):1972-1985, 2026 Related DOI : https://doi.org/10.1109/TFUZZ.2026.3683688 Focus to learn more DOI(s) linking to related resources Submission history From: Xiaowei Jiang [ view email ] [v1] Thu, 30 Jul 2026 02:44:54 UTC (2,898 KB) Full-text links: Access Paper: View a PDF of the paper titled iFuzz-Meta: An Interpretable Fuzzy Learning Framework Bridging Top-Down and Bottom-Up Knowledge Integration, by Xiaowei Jiang and 7 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2026-08 Change to browse by: cs cs.AI cs.HC References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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