Research Article
Max-Min Aggregation in Fuzzy Linguistic Systems and Machine Learning: A Narrative Review
@ARTICLE{10.4108/eetcasa.9751, author={Nguyen Van Han}, title={Max-Min Aggregation in Fuzzy Linguistic Systems and Machine Learning: A Narrative Review}, journal={EAI Endorsed Transactions on Contex-aware Systems and Applications}, volume={10}, number={1}, publisher={EAI}, journal_a={CASA}, year={2025}, month={7}, keywords={Linguistic Variables, Aggregation Operators, Explainable AI, Machine Learning, Max-Min Aggregation, Fuzzy logic, Fuzzy Linguistic Systems}, doi={10.4108/eetcasa.9751} }- Nguyen Van Han
Year: 2025
Max-Min Aggregation in Fuzzy Linguistic Systems and Machine Learning: A Narrative Review
CASA
EAI
DOI: 10.4108/eetcasa.9751
Abstract
Max-min aggregation functions play a fundamental role in fuzzy linguistic systems and machine learning by providing interpretable and mathematically sound methods for combining imprecise and qualitative information. This narrative review synthesizes the key concepts, models, and applications of max-min aggregation, highlighting its significance in enabling human-centric reasoning and explainable AI. We discuss theoretical foundations, linguistic modeling frameworks, and diverse practical applications, including decision support systems and fuzzy rule-based classifiers. Challenges such as scalability, integration with deep learning, and semantic standardization are identified, along with promising future research directions. This review aims to provide a comprehensive understanding of max-min aggregation’s contributions to interpretable and flexible AI systems
Copyright © 2025 Nguyen Van Han, licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
