@article{Landa-Torres2012,

title = {Evaluating the Internationalization Success of Companies Through a Hybrid Grouping Harmony Search\textemdashExtreme Learning Machine Approach},

author = {Itziar Landa-Torres and Emilio G Ortiz-Garcia and Sancho Salcedo-Sanz and Mar\'{i}a J Segovia-Vargas and Sergio Gil-Lopez and Marta Miranda and Jose M Leiva-Murillo and Javier Del Ser},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6200298},

issn = {1932-4553},

year  = {2012},

date = {2012-01-01},

journal = {IEEE Journal of Selected Topics in Signal Processing},

volume = {6},

number = {4},

pages = {388--398},

abstract = {The internationalization of a company is widely understood as the corporative strategy for growing through external markets. It usually embodies a hard process, which affects diverse activities of the value chain and impacts on the organizational structure of the company. There is not a general model for a successful international company, so the success of an internationalization procedure must be estimated based on different variables addressing the status, strategy and market characteristics of the company at hand. This paper presents a novel hybrid soft-computing approach for evaluating the internationalization success of a company based on existing past data. Specifically, we propose a hybrid algorithm composed by a grouping-based harmony search (HS) approach and an extreme learning machine (ELM) ensemble. The proposed hybrid scheme further incorporates a feature selection method, which is obtained by means of a given group in the HS encoding format, whereas the ELM ensemble renders the final accuracy metric of the model. Practical results for the proposed hybrid technique are obtained in a real application based on the exporting success of Spanish manufacturing companies, which are shown to be satisfactory in comparison with alternative state-of-the-art techniques.},

keywords = {Companies, Company internationalization, corporative strategy, diverse activity, Economics, Electronic mail, ensembles, exporting, exporting performance, external markets, extreme learning machine ensemble, extreme learning machines, feature selection method, grouping-based harmony search, hard process, harmony search (HS), hybrid algorithm, hybrid algorithms, hybrid grouping harmony search-extreme learning ma, hybrid soft computing, international company, international trade, internationalization procedure, internationalization success, learning (artificial intelligence), Machine learning, organizational structure, Signal processing algorithms, Spanish manufacturing company, Training, value chain},

pubstate = {published},

tppubtype = {article}

}