RT Conference Proceedings
T1 On the role of relations and interactions in fuzzy systems for machine learning
A1 Pérez-Sechi, Carlos Ignacio
A1 Gutiérrez García-Pardo, Inmaculada
A1 Castro Cantalejo, Javier
A1 Gómez González, Daniel
A1 Martín García, Daniel
A1 Espínola Vílchez, María Rosario
AB In 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 processes
SN 1544-5615
YR 2025
FD 2025
LK https://hdl.handle.net/20.500.14352/132205
UL https://hdl.handle.net/20.500.14352/132205
LA eng
NO C. 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/FUZZ62266.2025.11152169
NO Secretaría de Estado de Investigacion, Desarrollo e Innovacion
DS Docta Complutense
RD 21 feb 2026