Unsupervised machine learning approach for building composite indicators with fuzzy metrics
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Otros documentos de la autoría: Jiménez-Fernández, Eduardo; Sánchez Domínguez, Angeles; Sánchez Pérez, Enrique Alfonso
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Título
Unsupervised machine learning approach for building composite indicators with fuzzy metricsFecha de publicación
2022-04-04Editor
ElsevierCita bibliográfica
JIMÉNEZ-FERNÁNDEZ, E.; SÁNCHEZ, A.; PÉREZ, EA Sánchez. Unsupervised machine learning approach for building composite indicators with fuzzy metrics. Expert Systems with Applications, 2022, vol. 200, p. 116927.Tipo de documento
info:eu-repo/semantics/articleVersión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
This study aims at developing a new methodological approach for building composite indicators, focusing on the weight schemes through an unsupervised machine learning technique. The composite indicator proposed is ... [+]
This study aims at developing a new methodological approach for building composite indicators, focusing on the weight schemes through an unsupervised machine learning technique. The composite indicator proposed is based on fuzzy metrics to capture multidimensional concepts that do not have boundaries, such as competitiveness, development, corruption or vulnerability. This methodology is designed for formative measurement models using a set of indicators measured on different scales (quantitative, ordinal and binary) and it is partially compensatory. Under a benchmarking approach, the single indicators are synthesized. The optimization method applied manages to remove the overlapping information provided for the single indicators, so that the composite indicator provides a more realistic and faithful approximation to the concept which would be studied. It has been quantitatively and qualitatively validated with a set of randomized databases covering extreme and usual cases. [-]
Publicado en
Expert Systems with Applications, Vol. 200, August 2022Entidad financiadora
FEDER-University of Granada | Universitat Jaume I | Universidad de Granada / CBUA
Código del proyecto o subvención
B-SEJ-242.UGR20 | E-2018-03
Título del proyecto o subvención
An innovative methodological approach for measuring multidimensional poverty in Andalusia (COMPOSITE)
Derechos de acceso
© 2022 The Author(s). Published by Elsevier Ltd.
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
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