Título Evolutionary product-unit neural networks classifiers
Publicación externa No
Medio Neurocomputing
Alcance Article
Naturaleza Científica
Cuartil JCR 3
Cuartil SJR 2
Impacto JCR 1.234
Impacto SJR 0.514
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-55349128771&doi=10.1016%2fj.neucom.2007.11.019&partnerID=40&md5=e254691dd2ee0323cbadba4305481b65
Fecha de publicacion 01/01/2008
ISI 000261643700060
Scopus Id 2-s2.0-55349128771
DOI 10.1016/j.neucom.2007.11.019
Abstract This paper proposes a classification method based on a special class of feed-forward neural network, namely product-unit neural networks. Product-units are based on multiplicative nodes instead of additive ones, where the nonlinear basis functions express the possible strong interactions between variables. We apply an evolutionary algorithm to determine the basic structure of the product-unit model and to estimate the coefficients of the model. We use softmax transformation as the decision rule and the cross-entropy error function because of its probabilistic interpretation. The approach can be seen as nonlinear multinomial logistic regression where the parameters are estimated using evolutionary computation. The empirical and specific multiple comparison statistical test results, carried out over several benchmark data sets and a complex real microbial Listeria growth/no growth problem, show that the proposed model is promising in terms of its classification accuracy and the number of the model coefficients, yielding a state-of-the-art performance. © 2008 Elsevier B.V. All rights reserved.
Palabras clave Evolutionary algorithms; Feedforward neural networks; Ketones; Probability density function; Statistical tests; Trees (mathematics); Vegetation; Basic structures; Benchmark datums; Classification; Classification accuracies; Classification methods; Cross entropies; Decision rules; Error functions; Evolutionary computations; Evolutionary neural networks; Growth problems; Listeria; Model coefficients; Multinomial logistic regressions; Multiple comparisons; Neural networks classifiers; Nonlinear basis functions; Probabilistic interpretations; Product-unit neural networks; Special classes; Strong interactions; Neural networks; accuracy; analytical error; article; artificial neural network; bacterial growth; classification algorithm; classifier; diabetes mellitus; entropy; human; hypothyroidism; intermethod comparison; Listeria; mathematical model; nonhuman; nonlinear system; priority journal; probability; quality control
Miembros de la Universidad Loyola

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