Systematic Review of Max-Min Aggregation in Fuzzy Systems and Interpretable Machine Learning: Models, Evaluation, and Applications

Nguyen Van Han

doi:10.4108/eetcasa.9752

Authors

Nguyen Van Han Thuyloi University https://orcid.org/0000-0002-4054-565X

DOI:

https://doi.org/10.4108/eetcasa.9752

Keywords:

Systematic Review, Aggregation Operators, Linguistic Modeling, Interpretable Machine Learning, Max-Min Aggregation, Explainable Artifical Intelligence (XAI), Fuzzy Logic

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.

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