Symmetry/Asymmetry in Fuzzy Sets and Fuzzy Systems
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Mathematics".
Deadline for manuscript submissions: 30 June 2026 | Viewed by 32
Special Issue Editors
Interests: data mining; uncertain artificial intelligence; fuzzy computing; fuzzy system; multi-label learning
Interests: data mining; fuzzy computing; fuzzy system; artificial intelligence and pattern recognition; interpretability and fuzzy uncertainty learning; bioinformatics; uncertainty learning and modeling
Special Issue Information
Dear Colleagues,
In recent years, fuzzy systems have continued to demonstrate significant value in automatic control, pattern recognition, and decision support. Unlike traditional precise modeling, fuzzy systems represent inputs, outputs, and states with fuzzy sets and integrate fuzzy rules, fuzzy reasoning, and fuzzy logic—offering a unified paradigm for handling uncertainty, vagueness, and incompleteness. As data scale and complexity grow, symmetry/asymmetry becomes increasingly central to fuzzy-system design: it emerges in fuzzy similarity, dependency measures, membership-function construction, neighborhood and granularity structures, and the design of losses and regularizers (e.g., the structural symmetry between fuzzification and defuzzification).
This Special Issue focuses on methodological advances at the intersection of fuzzy–rough computing and granular computing with uncertainty learning. We particularly welcome cutting-edge research on uncertain information processing and intelligent reasoning, spanning the full spectrum from theoretical modeling to algorithm design and application-oriented validation, including—but not limited to—uncertainty modeling and learning, robust representation and feature selection, symmetric/asymmetric reasoning mechanisms, and their practice across diverse data types and tasks. We also encourage works that apply these methods to multi-label learning, graph/manifold similarity learning, multi-source information fusion, multimodal learning, and medical image analysis.
Dr. Tengyu Yin
Dr. Wei Zhang
Guest Editors
Manuscript Submission Information
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Keywords
- fuzzy systems
- fuzzy logic
- fuzzy reasoning
- fuzzy–rough sets
- fuzzy control
- symmetry and asymmetry
- artificial intelligence logic
- collaborative computing
- machine learning
- data mining
- computer vision
- granular computing
- multimodal learning
- biomedical applications
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