Advanced feature selection to study the internationalization strategy of enterprises

Abstract

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.

Keywords: Bagged decision trees; Evolutionary feature selection; Extreme learning machines; Internationaliza-tion; Multinational enterprises.

Figure 1. Flowchart of a standard genetic algorithm for wrapper feature selection.

Figure 2. Sample structure of a decision tree.

Figure 3. Sample topology of an ELM.

Figure 4. Boxplots of outputs from iterations on BDT, ELM—sig, and ELM—sin that have obtained the best results: (A) number of features (in magenta) and (B) average error (in red), standard deviation of the error (in green), and error of the best individual (in blue).