Advanced feature selection to study the internationalization strategy of enterprises

Firms face an increasingly complex economic and financial environment in which the access to international networks and markets is crucial. To be successful, companies need to understand the role of internationalization determinants such as bilateral psychic distance, experience, etc. Cutting-edge f...

Descripción completa

Detalles Bibliográficos
Autores: Herrero Cosío, Álvaro, Jiménez, Alfredo, Alcalde Delgado, Roberto
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Universidad de Burgos (UBU)
Repositorio:Repositorio Institucional de la Universidad de Burgos (RIUBU)
OAI Identifier:oai:riubu.ubu.es:10259/7234
Acceso en línea:http://hdl.handle.net/10259/7234
Access Level:acceso abierto
Palabra clave:Evolutionary feature selection
Bagged decision trees
Extreme learning machines
Internationaliza-tion
Multinational enterprises
Informática
Computer science
id ES_701d64e0bc0ab4ae10a649eae7b56b3d
oai_identifier_str oai:riubu.ubu.es:10259/7234
network_acronym_str ES
network_name_str España
repository_id_str
spelling Advanced feature selection to study the internationalization strategy of enterprisesHerrero Cosío, ÁlvaroJiménez, AlfredoAlcalde Delgado, RobertoEvolutionary feature selectionBagged decision treesExtreme learning machinesInternationaliza-tionMultinational enterprisesInformáticaComputer scienceFirms face an increasingly complex economic and financial environment in which the access to international networks and markets is crucial. To be successful, companies need to understand the role of internationalization determinants such as bilateral psychic distance, experience, etc. Cutting-edge feature selection methods are applied in the present paper and compared to previous results to gain deep knowledge about strategies for Foreign Direct Investment. More precisely, evolutionary feature selection, addressed from the wrapper approach, is applied with two different classifiers as the fitness function: Bagged Trees and Extreme Learning Machines. The proposed intelligent system is validated when applied to real-life data from Spanish Multinational Enterprises (MNEs). These data were extracted from databases belonging to the Spanish Ministry of Industry, Tourism, and Trade. As a result, interesting conclusions are derived about the key features driving to the internationalization of the companies under study. This is the first time that such outcomes are obtained by an intelligent system on internationalization data.The work was conducted during the research stays of Álvaro Herrero and Roberto Alcalde at KEDGE Business School in Bordeaux (France)PeerJ202320232021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10259/7234reponame:Repositorio Institucional de la Universidad de Burgos (RIUBU)instname:Universidad de Burgos (UBU)InglésPeerJ Computer Science. 2021, V. 7, e403https://doi.org/10.7717/peerj-cs.403Atribución 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riubu.ubu.es:10259/72342026-05-28T07:56:11Z
dc.title.none.fl_str_mv Advanced feature selection to study the internationalization strategy of enterprises
title Advanced feature selection to study the internationalization strategy of enterprises
spellingShingle Advanced feature selection to study the internationalization strategy of enterprises
Herrero Cosío, Álvaro
Evolutionary feature selection
Bagged decision trees
Extreme learning machines
Internationaliza-tion
Multinational enterprises
Informática
Computer science
title_short Advanced feature selection to study the internationalization strategy of enterprises
title_full Advanced feature selection to study the internationalization strategy of enterprises
title_fullStr Advanced feature selection to study the internationalization strategy of enterprises
title_full_unstemmed Advanced feature selection to study the internationalization strategy of enterprises
title_sort Advanced feature selection to study the internationalization strategy of enterprises
dc.creator.none.fl_str_mv Herrero Cosío, Álvaro
Jiménez, Alfredo
Alcalde Delgado, Roberto
author Herrero Cosío, Álvaro
author_facet Herrero Cosío, Álvaro
Jiménez, Alfredo
Alcalde Delgado, Roberto
author_role author
author2 Jiménez, Alfredo
Alcalde Delgado, Roberto
author2_role author
author
dc.subject.none.fl_str_mv Evolutionary feature selection
Bagged decision trees
Extreme learning machines
Internationaliza-tion
Multinational enterprises
Informática
Computer science
topic Evolutionary feature selection
Bagged decision trees
Extreme learning machines
Internationaliza-tion
Multinational enterprises
Informática
Computer science
description Firms face an increasingly complex economic and financial environment in which the access to international networks and markets is crucial. To be successful, companies need to understand the role of internationalization determinants such as bilateral psychic distance, experience, etc. Cutting-edge feature selection methods are applied in the present paper and compared to previous results to gain deep knowledge about strategies for Foreign Direct Investment. More precisely, evolutionary feature selection, addressed from the wrapper approach, is applied with two different classifiers as the fitness function: Bagged Trees and Extreme Learning Machines. The proposed intelligent system is validated when applied to real-life data from Spanish Multinational Enterprises (MNEs). These data were extracted from databases belonging to the Spanish Ministry of Industry, Tourism, and Trade. As a result, interesting conclusions are derived about the key features driving to the internationalization of the companies under study. This is the first time that such outcomes are obtained by an intelligent system on internationalization data.
publishDate 2021
dc.date.none.fl_str_mv 2021
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10259/7234
url http://hdl.handle.net/10259/7234
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv PeerJ Computer Science. 2021, V. 7, e403
https://doi.org/10.7717/peerj-cs.403
dc.rights.none.fl_str_mv Atribución 4.0 Internacional
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución 4.0 Internacional
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv PeerJ
publisher.none.fl_str_mv PeerJ
dc.source.none.fl_str_mv reponame:Repositorio Institucional de la Universidad de Burgos (RIUBU)
instname:Universidad de Burgos (UBU)
instname_str Universidad de Burgos (UBU)
reponame_str Repositorio Institucional de la Universidad de Burgos (RIUBU)
collection Repositorio Institucional de la Universidad de Burgos (RIUBU)
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869410557422993408
score 15,300719

Similares en LA Referencia