dc.creator | Tallón Ballesteros, Antonio Javier | es |
dc.creator | Riquelme Santos, José Cristóbal | es |
dc.creator | Ruiz Sánchez, Roberto | es |
dc.date.accessioned | 2023-05-08T09:31:13Z | |
dc.date.available | 2023-05-08T09:31:13Z | |
dc.date.issued | 2016 | |
dc.identifier.citation | Tallón Ballesteros, A.J., Riquelme Santos, J.C. y Ruiz Sánchez, R. (2016). Merging subsets of attributes to improve a hybrid consistency-based filter: a case of study in product unit neural networks. Connection Science, 28 (3), 242-257. https://doi.org/10.1080/09540091.2016.1149146. | |
dc.identifier.issn | 0954-0091 (impreso) | es |
dc.identifier.issn | 1360-0494 (online) | es |
dc.identifier.uri | https://hdl.handle.net/11441/145564 | |
dc.description.abstract | This paper presents a quality enhancement of the selected features by a hybrid filter-based jointly on feature ranking and feature subset selection (FR-FSS) using a consistency-based measure via merging new features which are obtained applying other FR-FSS evaluated with a correlation metric. The goal is to overcome the accuracy of a neural network classifier containing product units as hidden nodes combined with a feature selection pre-processing step by means of a single consistency-based FR-FSS filter. Neural models are trained with a refined evolutionary programming approach called two-stage evolutionary algorithm. The experimentation has been carried out in eight complex classification problems, seven out of them from UCI
(University of California at Irvine) repository and one real-world prob lem, with high test error rates (around 20%) with powerful classifiers such as 1-nearest neighbour or C4.5. Non-parametric statistical tests revealed that the new proposal significantly improves the accuracy of the neural models. | es |
dc.description.sponsorship | Comisión Interministerial de Ciencia y Tecnología TIN2011-28956-C02-02 | es |
dc.description.sponsorship | Comisión Interministerial de Ciencia y Tecnología TIN2014-55894-C2-R | es |
dc.description.sponsorship | Junta de Andalucía P11-TIC-7528 | es |
dc.format | application/pdf | es |
dc.format.extent | 16 | es |
dc.language.iso | eng | es |
dc.publisher | Taylor and Francis | es |
dc.relation.ispartof | Connection Science, 28 (3), 242-257. | |
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 | feature selection | es |
dc.subject | classification | es |
dc.subject | product units | es |
dc.subject | filters | es |
dc.subject | feature subset | es |
dc.subject | selection | es |
dc.title | Merging subsets of attributes to improve a hybrid consistency-based filter: a case of study in product unit neural networks | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | 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 | IN2011-28956-C02-02 | es |
dc.relation.projectID | TIN2014-55894-C2-R | es |
dc.relation.projectID | P11-TIC-7528 | es |
dc.relation.publisherversion | https://www.tandfonline.com/doi/abs/10.1080/09540091.2016.1149146?tab=permissions&scroll=top&role=tab&aria-labelledby=reprints-perm | es |
dc.identifier.doi | 10.1080/09540091.2016.1149146 | es |
dc.journaltitle | Connection Science | es |
dc.publication.volumen | 28 | es |
dc.publication.issue | 3 | es |
dc.publication.initialPage | 242 | es |
dc.publication.endPage | 257 | es |
dc.contributor.funder | Comisión Interministerial de Ciencia y Tecnología (CICYT). España | es |
dc.contributor.funder | Junta de Andalucía | es |