2026-05-05T00:02:40Zhttps://riubu.ubu.es/oai/request

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

A modified entropy-based performance criterion for class-modelling with multiple classes Valencia García, Olga Ortiz Fernández, Mª Cruz Sánchez Pastor, Mª Sagrario Sarabia Peinador, Luis Antonio Sensitivity Specificity Class-model Type I and Type II errors Entropy Benchmark Química analítica Chemistry, Analytic 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) 2021-10-22T12:28:05Z 2021-10-22T12:28:05Z 2021-10 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 0169-7439 http://hdl.handle.net/10259/6078 10.1016/j.chemolab.2021.104423 eng 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 application/pdf Elsevier