Unsupervised machine learning approach for building composite indicators with fuzzy metrics
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Other documents of the author: Jiménez-Fernández, Eduardo; Sánchez Domínguez, Angeles; Sánchez Pérez, Enrique Alfonso
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Title
Unsupervised machine learning approach for building composite indicators with fuzzy metricsDate
2022-04-04Publisher
ElsevierBibliographic citation
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.Type
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info:eu-repo/semantics/publishedVersionSubject
Abstract
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. [-]
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Expert Systems with Applications, Vol. 200, August 2022Funder Name
FEDER-University of Granada | Universitat Jaume I | Universidad de Granada / CBUA
Project code
B-SEJ-242.UGR20 | E-2018-03
Project title or grant
An innovative methodological approach for measuring multidimensional poverty in Andalusia (COMPOSITE)
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© 2022 The Author(s). Published by Elsevier Ltd.
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info:eu-repo/semantics/openAccess
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