Fuzzy and Hybrid Functional–Algebraic Models in Data Science
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D2: Operations Research and Fuzzy Decision Making".
Deadline for manuscript submissions: 27 February 2027 | Viewed by 167
Editors
Interests: fuzzy sets; fuzzy logic; similarity, association and correlation functions; fuzzy distribution sets; categorical distributions
Special Issues, Collections and Topics in MDPI journals
Interests: fuzzy sets; fuzzy logic; image processing
Interests: interval computations; uncertainty processing; foundations of heuristic techniques
Special Issues, Collections and Topics in MDPI journals
Interests: intelligent systems; robotics; control; systems and system of systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Data science addresses the analysis and decision-making processes based on real-world data that are inherently uncertain, vague, incomplete, and subjective. While probabilistic and statistical methods dominate many data science approaches, they primarily model randomness and often fail to capture graded, linguistic and subjective information, and non-additive uncertainty. Fuzzy sets and systems provide a rigorous mathematical framework for these phenomena and constitute a foundational component of data science.
Hybrid adaptive neuro-fuzzy inference systems represent some of the earliest systematic machine learning frameworks, combining functional fuzzy inference with data-driven parameter learning. These models predate many modern ML architectures and have been successfully applied in control and decision support problems.
Hybrid functional–algebraic models, including functions defined on algebraic structures and operators acting on functions, play a fundamental role in data science. They are based on mathematical representations such as probability distributions and mass functions, membership functions, interval and linguistic data, as well as on fuzzy set operators and aggregation functions, and similarity and correlation functions defined on data types endowed with algebraic operations.
This Special Issue aims to highlight mathematical foundations, functional–algebraic formulations, and hybrid models that contribute to data science. It invites original theoretical and applied papers with new results in this area.
Prof. Dr. Ildar Z. Batyrshin
Prof. Dr. Barnabas Bede
Prof. Dr. Vladik Kreinovich
Prof. Dr. Imre J. Rudas
Guest Editors
Manuscript Submission Information
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Keywords
- data science
- fuzzy set extensions
- fuzzy distribution sets
- categorical distributions
- uncertainty modeling
- aggregation functions
- information fusion
- similarity and correlation functions
- evaluation metrics
- adaptive inference systems
- interval analysis
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