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<title>A modified entropy-based performance criterion for class-modelling with multiple classes</title>
<creator>Valencia García, Olga</creator>
<creator>Ortiz Fernández, Mª Cruz</creator>
<creator>Sánchez Pastor, Mª Sagrario</creator>
<creator>Sarabia Peinador, Luis Antonio</creator>
<subject>Sensitivity</subject>
<subject>Specificity</subject>
<subject>Class-model</subject>
<subject>Type I and Type II errors</subject>
<subject>Entropy</subject>
<subject>Benchmark</subject>
<description>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.&#xd;
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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.&#xd;
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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.&#xd;
&#xd;
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).&#xd;
&#xd;
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.&#xd;
&#xd;
Illustration of the application of the index is shown with an experimental data set with four categories.</description>
<date>2021-10-22</date>
<date>2021-10-22</date>
<date>2021-10</date>
<type>info:eu-repo/semantics/article</type>
<identifier>0169-7439</identifier>
<identifier>http://hdl.handle.net/10259/6078</identifier>
<identifier>10.1016/j.chemolab.2021.104423</identifier>
<language>eng</language>
<relation>Chemometrics and Intelligent Laboratory Systems. 2021, V. 217, 104423</relation>
<relation>https://doi.org/10.1016/j.chemolab.2021.104423</relation>
<relation>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</relation>
<relation>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</relation>
<rights>http://creativecommons.org/licenses/by-nc-nd/4.0/</rights>
<rights>info:eu-repo/semantics/openAccess</rights>
<rights>Attribution-NonCommercial-NoDerivatives 4.0 Internacional</rights>
<publisher>Elsevier</publisher>
</thesis></metadata></record></GetRecord></OAI-PMH>