Self-adaptive MOEA feature selection for classification of bankruptcy prediction data

Detalhes bibliográficos
Autor(a) principal: Gaspar-Cunha, A.
Data de Publicação: 2014
Outros Autores: Recio, Gustavo, Costa, L., Estébanez, C.
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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spelling 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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