Título Non-linear multiclassifier model based on Artificial Intelligence to predict research and development performance in European countries
Autores de la Paz-Marín M., CAMPOY MUÑOZ, MARÍA DEL PILAR, Hervás-Martínez C., CAMPOY MUÑOZ, MARÍA DEL PILAR
Publicación externa No
Medio Technol. Forecast. Soc. Chang.
Alcance Article
Naturaleza Científica
Cuartil JCR 1
Cuartil SJR 1
Impacto JCR 2.10600
Impacto SJR 1.48300
Ámbito Internacional
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-84867099286&doi=10.1016%2fj.techfore.2012.06.001&partnerID=40&md5=dd122c9d0ccbcb9706c4a13f5e570da2
Fecha de publicacion 01/01/2012
ISI 000310667500013
Scopus Id 2-s2.0-84867099286
DOI 10.1016/j.techfore.2012.06.001
Abstract This paper deals with one of the most important keys for economic growth: scientific knowledge and innovation, following the linear Research and Development (R&D) model. Patents, scientific publications and expenditure in R&D as well as the personnel involved in these activities are taken into account as proxy indicators, together with variables related to education and economy in order to classify R&D performance in 25 European Union (EU) Member States. This study classifies these countries using a set of variables which characterize them from 2005 to 2008 and analyses the most relevant ones for this classification. The Multilayer Perceptron Model (MLP) and the Product-Unit Neural Network (EPUNN) models, both trained by evolutionary algorithms (EA), were used to classify yearly country observations in clusters previously defined by employing unsupervised algorithm k-means clustering, obtaining four different classes of national R&D performance: low, moderate, high and innovation driven economies. Finally, our methodology is compared to other classification methods normally used in machine learning. The results show that while various methods of classification exist, our methodology obtains models with a significantly lower number of coefficients without decreasing their accuracy in predicting the classification of other European countries or in these countries in the following years. © 2012 Elsevier Inc.
Palabras clave Classification methods; Economic growths; European Countries; European union; Innovation-driven economy; K-means clustering; Model-based OPC; Multi layer perceptron; Multi-classification; Multi-classi
Miembros de la Universidad Loyola

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