Título Evolutionary Product Unit Logistic Regression: The Case of Agrarian Efficiency
Autores GARCÍA ALONSO, CARLOS, Hervas-Martinez, Cesar, MILLÁN LARA, SALUD, TORRES JIMÉNEZ, MERCEDES, GARCÍA ALONSO, CARLOS, MILLÁN LARA, SALUD, TORRES JIMÉNEZ, MERCEDES
Publicación externa No
Medio Lect. Notes Comput. Sci.
Alcance Proceedings Paper
Naturaleza Científica
Cuartil JCR 4
Cuartil SJR 2
Impacto SJR 0.36900
Ámbito Internacional
Fecha de publicacion 01/01/2015
ISI 000367709100009
Scopus Id 2-s2.0-84952673352
DOI 10.1007/978-3-319-24598-0_9
Abstract By using a high-variability sample of real agrarian enterprises previously classified into two classes (efficient and inefficient), a comparative study was carried out to demonstrate the classification accuracy of logistic regression algorithms based on evolutionary product-unit neural networks. Data envelopment analysis considering variable returns-to-scale (BBC-DEA) was chosen to classify selected farms (220 olive tree farms in dry farming) as efficient or inefficient by using surveyed socio-economic variables (agrarian year 2000). Once the sample was grouped by BCC-DEA, easy-to-collect descriptive variables (concerning the farm and farmer) were then used as independent variables in order to find a quick and reliable alternative for classifying agrarian enterprises as efficient or inefficient. Results showed that our proposal is very promising for the classification of complex structures (farms).
Palabras clave Neural networks; Classification; Product-Unit; Evolutionary algorithms; Agrarian technical efficiency
Miembros de la Universidad Loyola

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