RT info:eu-repo/semantics/article
T1 Advanced feature selection to study the internationalization strategy of enterprises
A1 Herrero Cosío, Álvaro
A1 Jiménez, Alfredo
A1 Alcalde Delgado, Roberto
K1 Evolutionary feature selection
K1 Bagged decision trees
K1 Extreme learning machines
K1 Internationaliza-tion
K1 Multinational enterprises
K1 Informática
K1 Computer science
AB Firms face an increasingly complex economic and financial environment in whichthe access to international networks and markets is crucial. To be successful,companies need to understand the role of internationalization determinants suchas bilateral psychic distance, experience, etc. Cutting-edge feature selectionmethods are applied in the present paper and compared to previous results to gaindeep knowledge about strategies for Foreign Direct Investment. More precisely,evolutionary feature selection, addressed from the wrapper approach, is applied withtwo different classifiers as the fitness function: Bagged Trees and Extreme LearningMachines. The proposed intelligent system is validated when applied to real-lifedata from Spanish Multinational Enterprises (MNEs). These data were extractedfrom databases belonging to the Spanish Ministry of Industry, Tourism, and Trade.As a result, interesting conclusions are derived about the key features driving to theinternationalization of the companies under study. This is the first time that suchoutcomes are obtained by an intelligent system on internationalization data.
PB PeerJ
SN 2376-5992
YR 2021
FD 2021-03
LK http://hdl.handle.net/10259/7234
UL http://hdl.handle.net/10259/7234
LA eng
NO The work was conducted during the research stays of Álvaro Herrero and Roberto Alcalde at KEDGE Business School in Bordeaux (France)
DS Repositorio Institucional de la Universidad de Burgos
RD 25-abr-2024