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oai:riubu.ubu.es:10259/60782022-11-24T11:52:57Zcom_10259_6164com_10259_4534com_10259.4_106com_10259_2604com_10259_4249com_10259_5086col_10259_6165col_10259_4250

Valencia García, Olga 759 500 Ortiz Fernández, Mª Cruz 406 500 Sánchez Pastor, Mª Sagrario 518 500 Sarabia Peinador, Luis Antonio 534 500 2021-10-22T12:28:05Z 2021-10-22T12:28:05Z 2021-10 0169-7439 http://hdl.handle.net/10259/6078 10.1016/j.chemolab.2021.104423 The paper presents a new proposal for a single overall measure, the diagonal modified confusion entropy (DMCEN), to assess the performance of class-models jointly computed for several classes, a versatile index regarding sensitivity and specificity, and that supports class weighting. The characteristics of the proposed figure of merit are illustrated as against other usual performance measures and show how the index is more sensitive to the variations in the class-models than similar published indexes. Besides, a benchmark value representing a random modelling is also defined for DMCEN to be used as initial level to assess the quality of the built class-models. Furthermore, systematic comparisons have been conducted by using the degree of consistency C and the degree of discriminancy D when comparing the proposed DMCEN to the usual total efficiency (a geometric mean between sensitivity and specificity). Simulations show that, for a hundred thousand sensitivity/specificity matrices with four categories, C is almost 0.7 on average, well above the needed 0.5, and there is more than 62% probability that DMCEN detects differences when the total efficiency does not. Illustration of the application of the index is shown with an experimental data set with four categories. Spanish Ministerio de Ciencia, Innovaci on y Universidades (AEI) and Consejería de Educación de la Junta de Castilla y León through projects CTQ2017-88894-R and BU052P20 respectively (both co-financed with European Regional Development Funds) application/pdf eng Elsevier Chemometrics and Intelligent Laboratory Systems. 2021, V. 217, 104423 https://doi.org/10.1016/j.chemolab.2021.104423 info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/CTQ2017-88894-R/ES/NUEVAS HERRAMIENTAS QUIMIOMETRICAS CON VARIABLES LATENTES PARA LA TOMA DE DECISIONES EN TECNOLOGIA ANALITICA DE PROCESOS Y EN CONTEXTOS REGULADOS DE SEGURIDAD ALIMENTARIA info:eu-repo/grantAgreement/Junta de Castilla y León//BU052P20//Nuevos desarrollos metodológicos del diseño de experimentos para análisis químicos, bioquímicos y en tecnología analítica de procesos Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess Sensitivity Specificity Class-model Type I and Type II errors Entropy Benchmark Química analítica Chemistry, Analytic A modified entropy-based performance criterion for class-modelling with multiple classes info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Chemometrics and Intelligent Laboratory Systems 217 104423 THUMBNAIL Valencia-cils_2021.pdf.jpg Valencia-cils_2021.pdf.jpg IM Thumbnail image/jpeg 3456 https://riubu.ubu.es/bitstream/10259/6078/4/Valencia-cils_2021.pdf.jpg dace23dfb9af2885af9c55026fa54b31 MD5 4 LICENSE license.txt license.txt text/plain; charset=utf-8 998 https://riubu.ubu.es/bitstream/10259/6078/3/license.txt aa90105e1c90a1b4fe810649aa967b7d MD5 3 CC-LICENSE license_rdf license_rdf application/rdf+xml; charset=utf-8 805 https://riubu.ubu.es/bitstream/10259/6078/2/license_rdf 4460e5956bc1d1639be9ae6146a50347 MD5 2 ORIGINAL Valencia-cils_2021.pdf Valencia-cils_2021.pdf application/pdf 1583362 https://riubu.ubu.es/bitstream/10259/6078/1/Valencia-cils_2021.pdf 5bf4fac00dc226853b6ae5f19778014f MD5 1 10259/6078 oai:riubu.ubu.es:10259/6078 2022-11-24 12:52:57.056 Repositorio Institucional de la Universidad de Burgos bubrep@ubu.es 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