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.
