Pérez-Sechi, Carlos IgnacioGutiérrez García-Pardo, InmaculadaCastro Cantalejo, JavierGómez González, DanielMartín García, DanielEspínola Vílchez, María Rosario2026-02-122026-02-122025C. I. Pérez-SechI, I. Gutiérrez, J. Castro, D. Gómez, D. Martín and R. Espínola, "On the role of relations and interactions in fuzzy systems for machine learning*," 2025 IEEE International Conference on Fuzzy Systems (FUZZ), Reims, France, 2025, pp. 1-6, doi: 10.1109/FUZZ62266.2025.111521691544-561510.1109/FUZZ62266.2025.11152169https://hdl.handle.net/20.500.14352/132205In this study, we address the challenge of explainability in machine learning within the context of multi-agent fuzzy systems. Specifically, we present two advances. On one hand, based on the Kruskal-Wallis test, we define the KWSHAP algorithm, which is applicable to any machine learning model. On the other hand, we introduce a method to define variables in a machine learning model when the input includes several fuzzy measures defined over a set of agents. This information is incorporated into the model through the calculation of the Shapley value and interaction indices. The KWSHAP algorithm and the subsequent graph representation allow us to interpret the role and importance of these interactions. Our findings enhance machine learning interpretability, offering a statistically robust and flexible framework for understanding relationships among features, thus contributing to more transparent and trustworthy decision-making processesengAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/conference paper1558-4739https://doi.org/10.1109/FUZZ62266.2025.11152169https://ieeexplore.ieee.org/document/11152169restricted access519.251004.6Fuzzy MeasuresInteraction IndexMachine LearningMulti-Agent Fuzzy SystemsShapley ValueXAIEstadística aplicadaMatemáticas (Matemáticas)Inteligencia artificial (Informática)1209 Estadística12 Matemáticas1203.04 Inteligencia Artificial