Research Article
Systematic Review of Max-Min Aggregation in Fuzzy Systems and Interpretable Machine Learning: Models, Evaluation, and Applications
@ARTICLE{10.4108/eetcasa.9752, author={Nguyen Van Han}, title={Systematic Review of Max-Min Aggregation in Fuzzy Systems and Interpretable Machine Learning: Models, Evaluation, and Applications}, journal={EAI Endorsed Transactions on Contex-aware Systems and Applications}, volume={10}, number={1}, publisher={EAI}, journal_a={CASA}, year={2025}, month={7}, keywords={Systematic Review, Aggregation Operators, Linguistic Modeling, Interpretable Machine Learning, Max-Min Aggregation, Explainable Artifical Intelligence (XAI), Fuzzy Logic}, doi={10.4108/eetcasa.9752} }- Nguyen Van Han
Year: 2025
Systematic Review of Max-Min Aggregation in Fuzzy Systems and Interpretable Machine Learning: Models, Evaluation, and Applications
CASA
EAI
DOI: 10.4108/eetcasa.9752
Abstract
This systematic review investigates the use of max-min aggregation in fuzzy systems and interpretable machine learning. Rooted in fuzzy set theory and triangular norms, max-min aggregation offers a transparent and mathematically simple approach to modeling uncertainty and decision-making. We examine theoretical foundations, practical applications, evaluation methods, and comparative taxonomies. The review identifies key challenges such as scalability and integration with learning algorithms, and highlights future directions for improving transparency in AI. Our findings underscore the relevance of max-min aggregation in developing interpretable and responsible 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.
