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   <ow:Publication rdf:about="oai:digibug.ugr.es:10481/74429">
      <dc:title>Unsupervised machine learning approach for building composite indicators with fuzzy metrics</dc:title>
      <dc:creator>Sánchez Domínguez, María Ángeles</dc:creator>
      <dc:creator>Jiménez Fernández, Eduardo</dc:creator>
      <dc:creator>Sánchez Pérez, Enrique Alfonso</dc:creator>
      <dc:subject>Machine learning</dc:subject>
      <dc:subject>Fuzzy metric</dc:subject>
      <dc:subject>Composite indicators</dc:subject>
      <dc:subject>Benchmarking</dc:subject>
      <dc:subject>Robustness and sensitivity analysis</dc:subject>
      <dc:description>This work was supported by the project FEDER-University of Granada ( B-SEJ-242.UGR20 ), 2021-2023: An innovative methodological approach for measuring multidimensional poverty in Andalusia (COMPOSITE). Eduardo Jiménez-Fernández would also like to thank the support received from Universitat Jaume I under the grant E-2018-03 .</dc:description>
      <dc:description>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.</dc:description>
      <dc:date>2022-04-21T08:59:10Z</dc:date>
      <dc:date>2022-04-21T08:59:10Z</dc:date>
      <dc:date>2022-08-15</dc:date>
      <dc:type>info:eu-repo/semantics/article</dc:type>
      <dc:identifier>E. Jiménez-Fernández et al. Unsupervised machine learning approach for building composite indicators with fuzzy metrics. Expert Systems With Applications 200 (2022) 116927. [https://doi.org/10.1016/j.eswa.2022.116927]</dc:identifier>
      <dc:identifier>http://hdl.handle.net/10481/74429</dc:identifier>
      <dc:identifier>10.1016/j.eswa.2022.116927</dc:identifier>
      <dc:language>eng</dc:language>
      <dc:rights>http://creativecommons.org/licenses/by/3.0/es/</dc:rights>
      <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
      <dc:rights>Atribución 3.0 España</dc:rights>
      <dc:publisher>Elsevier</dc:publisher>
   </ow:Publication>
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