Self-adaptive MOEA feature selection for classification of bankruptcy prediction data
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/1822/30539 |
Resumo: | Article ID 314728 |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Self-adaptive MOEA feature selection for classification of bankruptcy prediction dataBankruptcy predictionFeature selectionScience & TechnologyArticle ID 314728Bankruptcy prediction is a vast area of finance and accounting whose importance lies in the relevance for creditors and investors in evaluating the likelihood of getting into bankrupt. As companies become complex, they develop sophisticated schemes to hide their real situation. In turn, making an estimation of the credit risks associated with counterparts or predicting bankruptcy becomes harder. Evolutionary algorithms have shown to be an excellent tool to deal with complex problems in finances and economics where a large number of irrelevant features are involved.This paper provides a methodology for feature selection in classification of bankruptcy data sets using an evolutionary multiobjective approach that simultaneously minimise the number of features and maximise the classifier quality measure (e.g., accuracy).The proposed methodology makes use of self-adaptation by applying the feature selection algorithm while simultaneously optimising the parameters of the classifier used. The methodology was applied to four different sets of data. The obtained results showed the utility of using the self-adaptation of the classifier.This work was partially supported by the Portuguese Foundation for Science and Technology under Grant PEst-C/CTM/LA0025/2011 (Strategic Project-LA 25-2011-2012) and by the Spanish Ministerio de Ciencia e Innovacion, under the project "Gestion de movilidad efficiente y sostenible, MOVES" with Grant Reference TIN2011-28336.Hindawi Publishing CorporationUniversidade do MinhoGaspar-Cunha, A.Recio, GustavoCosta, L.Estébanez, C.20142014-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/30539eng2356-614010.1155/2014/31472824707201info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T12:23:53Zoai:repositorium.sdum.uminho.pt:1822/30539Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:17:43.731401Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Self-adaptive MOEA feature selection for classification of bankruptcy prediction data |
title |
Self-adaptive MOEA feature selection for classification of bankruptcy prediction data |
spellingShingle |
Self-adaptive MOEA feature selection for classification of bankruptcy prediction data Gaspar-Cunha, A. Bankruptcy prediction Feature selection Science & Technology |
title_short |
Self-adaptive MOEA feature selection for classification of bankruptcy prediction data |
title_full |
Self-adaptive MOEA feature selection for classification of bankruptcy prediction data |
title_fullStr |
Self-adaptive MOEA feature selection for classification of bankruptcy prediction data |
title_full_unstemmed |
Self-adaptive MOEA feature selection for classification of bankruptcy prediction data |
title_sort |
Self-adaptive MOEA feature selection for classification of bankruptcy prediction data |
author |
Gaspar-Cunha, A. |
author_facet |
Gaspar-Cunha, A. Recio, Gustavo Costa, L. Estébanez, C. |
author_role |
author |
author2 |
Recio, Gustavo Costa, L. Estébanez, C. |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Gaspar-Cunha, A. Recio, Gustavo Costa, L. Estébanez, C. |
dc.subject.por.fl_str_mv |
Bankruptcy prediction Feature selection Science & Technology |
topic |
Bankruptcy prediction Feature selection Science & Technology |
description |
Article ID 314728 |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014 2014-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/1822/30539 |
url |
http://hdl.handle.net/1822/30539 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2356-6140 10.1155/2014/314728 24707201 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Hindawi Publishing Corporation |
publisher.none.fl_str_mv |
Hindawi Publishing Corporation |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799132629850652672 |