Title Classification of countries' progress toward a knowledge economy based on machine learning classification techniques
Authors de la Paz-Marin, Monica, Gutierrez, Pedro Antonio, Hervas-Martinez, Cesar
External publication No
Means Expert Syst. Appl.
Scope Article
Nature Científica
JCR Quartile 1
SJR Quartile 1
JCR Impact 2.98100
SJR Impact 1.47300
Area International
Publication date 01/01/2015
ISI 000344034300047
Scopus Id 2-s2.0-84907513648
DOI 10.1016/j.eswa.2014.08.008
Abstract Knowledge is a key factor of competitive advantages in the current economic crisis and uncertain environment. There are a number of indicators to measure knowledge advances, however, the benefits for stakeholders and policy makers are limited because of a lack of classification models. This paper introduces an approach to classify 54 countries (in 2007-2009) according to their progress toward a knowledge economy (KE). To achieve this, the aims of this paper are twofold: first, to find clusters of countries at a similar stage of development toward ICE to test if they are meaningful; hence, it will be possible to order the clusters from early KEs (last cluster) to advanced KEs (first cluster). Second, having obtained these clusters, it is possible to build various models to detect the advancement of countries toward ICE from one year to another due to its classification. Then, three ordinal classifiers from the machine-learning field were compared in order to select the classifier that performs the best and to confirm the ordinal description of the clusters. Finally, an ordinal model based on the Support Vector Ordinal Regression with Implicit Constraints was selected because of its ability to classify the patterns into the clusters, confirming the appropriateness of the clusters and their ordinal nature. The proposed ordinal classifier could be used for monitoring the progress or stage of transition to ICE and for analysing whether a country changes clusters, entering one that performs better or worse. (C) 2014 Elsevier Ltd. All rights reserved.
Keywords Decision support systems; Machine learning; Knowledge economy; Hierarchical clustering; Ordinal classification
Universidad Loyola members