Herrero Álvaro, Jiménez Alfredo, Alcalde Roberto
Departamento de Ingeniería Informática, Universidad de Burgos, Burgos, Spain.
Department of Management, KEDGE Business School, Bordeaux, France.
PeerJ Comput Sci. 2021 Mar 25;7:e403. doi: 10.7717/peerj-cs.403. eCollection 2021.
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.
企业面临着日益复杂的经济和金融环境，在这种环境中，进入国际网络和市场至关重要。为了取得成功，公司需要了解双边心理距离、经验等国际化决定因素的作用。本文应用了前沿的特征选择方法，并与先前的结果进行比较，以深入了解外国直接投资战略。更确切地说，从包装器方法出发的进化特征选择，与两种不同的分类器作为适应度函数一起应用：袋装树和极限学习机。当将所提出的智能系统应用于西班牙跨国企业（MNEs）的实际数据时，该系统得到验证。这些数据是从属于西班牙工业、旅游和贸易部的数据库中提取的。结果，得出了关于推动所研究公司国际化的关键特征的有趣结论。这是智能系统首次从国际化数据中获得此类结果。
