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Evolutionary feature selection approaches for insolvency business prediction with genetic programming
dc.contributor.author | Beade, Angel | |
dc.contributor.author | Rodríguez López, Manuel | |
dc.contributor.author | Santos Reyes, José | |
dc.date.accessioned | 2024-06-13T11:09:03Z | |
dc.date.available | 2024-06-13T11:09:03Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | Beade, A., Rodríguez López, M. & Santos Reyes, J. (2023). Evolutionary feature selection approaches for insolvency business prediction with genetic programming. Natural computing, 22, 705-722. 10.1007/S11047-023-09951-4 | es_ES |
dc.identifier.issn | 1567-7818 | |
dc.identifier.uri | http://hdl.handle.net/2183/36888 | |
dc.description.abstract | [Abstract]: This study uses different feature selection methods in the field of business failure prediction and tests the capability of Genetic Programming (GP) as an appropriate classifier in this field. The prediction models categorize the insolvency/noninsolvency of a firm one year in advance from a large set of financial ratios. Different selection strategies based on two evolutionary algorithms were used to reduce the dimensionality of the financial features considered. The first method considers the combination between the global search provided by an evolutionary algorithm (differential evolution) with a simple classifier, together with the possible use of classical filters in a first step of feature selection. Secondly, genetic programming is used as a feature selector. In addition, these selection approaches will be compared when GP is used exclusively as a classifier. The results show that, when using GP as a classifier method, the proposed selection method with GP stands out from the rest. Moreover, the use of GP as a classifier improves the results with respect to other classifier methods. This shows an added value to the use of GP in this field, in addition to the interpretability of GP prediction models. | es_ES |
dc.description.sponsorship | Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This study was funded by the Xunta de Galicia and the European Union (European Regional Development Fund—Galicia 2014–2020 Program), with Grants CITIC (ED431G 2019/01) and GPC ED431B 2022/33, and by the Spanish Ministry of Science and Innovation (project PID2020-116201GB-I00). | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431B 2022/33 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Springer | es_ES |
dc.relation | info:eu-repo/gratAgreement/AEI/ Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/project PID2020-116201GB-I00/ES/ RAZONAMIENTO AUTOMATICO Y APRENDIZAJE CON INDUCCION DE CONOCIMIENTO | es_ES |
dc.relation.uri | 10.1007/S11047-023-09951-4 | es_ES |
dc.rights | Atribución 4.0 Internacional | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Differential evolution | es_ES |
dc.subject | Genetic programming | es_ES |
dc.subject | Feature selection | es_ES |
dc.subject | Prediction of business insolvency | es_ES |
dc.title | Evolutionary feature selection approaches for insolvency business prediction with 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 | Natural Computing | es_ES |
UDC.volume | 22 | es_ES |
UDC.startPage | 705 | es_ES |
UDC.endPage | 722 | es_ES |
dc.identifier.doi | 10.1007/S11047-023-09951-4 |
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