RT info:eu-repo/semantics/article T1 Network-based quality index aggregation in the retail location problem. A supervised learning approach T2 Agregación de índices de calidad basados en redes en el problema de localización de comercios minoristas. Un enfoque desde el aprendizaje supervisado A1 Ahedo García, Virginia A1 Santos Martín, José Ignacio A1 Galán Ordax, José Manuel K1 Complex networks K1 Retail location problem K1 Prediction K1 Knowledge transfer K1 Classification K1 Pattern recognition K1 Redes complejas K1 Problema de localización de comercios K1 Predicción K1 Transferencia de conocimiento K1 Clasificación K1 Reconocimiento de patrones K1 Gestión de empresas K1 Industrial management K1 Comercio K1 Commerce K1 Estadística matemática K1 Mathematical statistics AB In retailing, the location problem is a fundamental strategic aspect. It is usually formalized as a multi-criteria optimization problem to choose the most appropriate spot. A relevant element in the selection is the adequacy of the commercial ecosystem in the vicinity of the location. To account for this criterion, there are different primary indices based on networks that formalize the quality of the available options with regard to the surrounding ecosystem. Previous research suggests that aggregating the different indices using a classifier can improve the quality of these metrics. In this paper, we compare different classifiers to assess their performance in that respect. The analysis has been performed in a context of transfer knowledge and information fusion using data from all the cities in Castile and Leon, Spain. Our results show that the random forest and generalized linear models obtain results significantly superior to other alternatives. PB ADINGOR SN 1132-175X YR 2024 FD 2024-07 LK http://hdl.handle.net/10259/9520 UL http://hdl.handle.net/10259/9520 LA eng NO The authors acknowledge financial support from the Spanish Ministry of Science, Innovation and Universities (Excellence Network RED2018‐102518‐T), the Spanish State Research Agency (PID2020-118906GB-I00/AEI/10.13039/501100011033) and the Fundación Bancaria Caixa D. Estalvis I Pensions de Barcelona, La Caixa (2020/00062/001). In addition, we acknowledge support from the Santander Supercomputación group (University of Cantabria), that provided access to the Altamira Supercomputer —located at the Institute of Physics of Cantabria (IFCA-CSIC) and member of the Spanish Supercomputing Network— to perform the different simulations/analyses. DS Repositorio Institucional de la Universidad de Burgos RD 06-ene-2025