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Variable selection in the prediction of business failure using genetic programming
dc.contributor.author | Beade, José | |
dc.contributor.author | Rodríguez Seijo, José Manuel | |
dc.contributor.author | Santos Reyes, José | |
dc.date.accessioned | 2024-04-18T07:50:48Z | |
dc.date.available | 2024-04-18T07:50:48Z | |
dc.date.issued | 2024-04-08 | |
dc.identifier.citation | Á. Beade, M. Rodríguez, y J. Santos, «Variable selection in the prediction of business failure using genetic programming», Knowledge-Based Systems, vol. 289, p. 111529, abr. 2024, doi: 10.1016/j.knosys.2024.111529. | es_ES |
dc.identifier.issn | 1872-7409 | |
dc.identifier.issn | 0950-7051 | |
dc.identifier.uri | http://hdl.handle.net/2183/36239 | |
dc.description.abstract | This study focuses on dimensionality reduction by variable selection in business failure prediction models. A new method of dimensionality reduction by variable selection using Genetic Programming is proposed, which takes into account the relative frequency of occurrence of the explanatory variables in the evolved solutions, as well as the statistical relevance of that frequency. For a better evaluation of the proposed method and its comparison with other well-tested and widely used variable selection methods, the prediction of business failure in three temporal horizons (1, 5 and 9 years prior to failure) is considered. Additionally, a comparison of the sets of variables selected with different feature selection methods is performed, also considering different classifiers in the comparison, among which Genetic Programming is included as a classifier. The results indicate that the proposed method (using Genetic Programming as a variable selection method) is superior to the most tested and widely used methods analyzed, and this superiority increases if Genetic Programming is also used as a classification method. | es_ES |
dc.description.sponsorship | This study was funded by the European Union (European Regional Development Fund - Galicia 2014-2020 Program) and Xunta de Galicia, with grants GPC ED431B 2022/33 and CITIC (ED431G 2019/01), and by the Spanish Ministry of Science and Innovation (project PID2020-116201GB-I00). | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431B 2022/33 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier B.V. | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-116201GB-I00/ES/RAZONAMIENTO AUTOMATICO Y APRENDIZAJE CON INDUCCION DE CONOCIMIENTO/ | es_ES |
dc.relation.uri | https://doi.org/10.1016/j.knosys.2024.111529 | es_ES |
dc.rights | Atribución-NoComercial-SinDerivadas 3.0 España | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.subject | Business failure | es_ES |
dc.subject | Dimensionality reduction | es_ES |
dc.subject | Feature selection | es_ES |
dc.subject | Evolutionary computation | es_ES |
dc.subject | Genetic programming | es_ES |
dc.title | Variable selection in the prediction of business failure using genetic programming | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Knowledge-Based Systems | es_ES |
UDC.volume | 289 | es_ES |
UDC.startPage | 111529 | es_ES |
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