dc.creator | Tallón Ballesteros, Antonio Javier | es |
dc.creator | Hervás Martínez, César | es |
dc.creator | Riquelme Santos, José Cristóbal | es |
dc.creator | Ruiz, Roberto | es |
dc.date.accessioned | 2016-07-13T08:15:44Z | |
dc.date.available | 2016-07-13T08:15:44Z | |
dc.date.issued | 2013 | |
dc.identifier.citation | Tallón Ballesteros, A.J., Hervás Martínez, C., Riquelme Santos, J.C. y Ruíz, R. (2013). Feature selection to enhance a two-stage evolutionary algorithm in product unit neural networks for complex classification problems. Neurocomputing, 114, 107-117. | |
dc.identifier.issn | 0925-2312 | es |
dc.identifier.uri | http://hdl.handle.net/11441/43538 | |
dc.description.abstract | This paper combines feature selection methods with a two-stage evolutionary classifier based on product unit neural
networks. The enhanced methodology has been tried out with four filters using 18 data sets that report test error
rates about 20 % or above with reference classifiers such as C4.5 or 1-NN. The proposal has also been evaluated in a
liver-transplantation real-world problem with serious troubles in the data distribution and classifiers get low
performance. The study includes an overall empirical comparison between the models obtained with and without
feature selection using different kind of neural networks, like RBF, MLP and other state-of-the-art classifiers.
Statistical tests show that our proposal significantly improves the test accuracy of the previous models. The reduction
percentage in the number of inputs is, on average, above 55 %, thus a greater efficiency is achieved. | es |
dc.description.sponsorship | MICYT TIN2007-68084- C02-02 | es |
dc.description.sponsorship | MICYT TIN2008-06681-C06-03 | es |
dc.description.sponsorship | MICYT TIN2011-28956-C02 | es |
dc.format | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | Neurocomputing, 114, 107-117. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Artificial neural networks | es |
dc.subject | Product units | es |
dc.subject | evolutionary algorithms | es |
dc.subject | classification | es |
dc.subject | Feature selection | es |
dc.subject | High error problems | es |
dc.title | Feature selection to enhance a two-stage evolutionary algorithm in product unit neural networks for complex classification problems | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.projectID | TIN2007-68084- C02-02 | es |
dc.relation.projectID | TIN2008-06681-C06-03 | es |
dc.relation.projectID | TIN2011-28956-C02 | es |
dc.identifier.doi | http://dx.doi.org/10.1016/j.neucom.2012.08.041 | es |
idus.format.extent | 11 | es |
dc.journaltitle | Neurocomputing | es |
dc.publication.volumen | 114 | es |
dc.publication.initialPage | 107 | es |
dc.publication.endPage | 117 | es |
dc.identifier.idus | https://idus.us.es/xmlui/handle/11441/43538 | |