Título Logistic evolutionary product-unit neural network classifier: the case of agrarian efficiency
Autores TORRES JIMÉNEZ, MERCEDES, GARCÍA ALONSO, CARLOS, SÁNCHEZ MONEDERO, JAVIER, MILLÁN LARA, SALUD, Hervas-Martinez, Cesar
Publicación externa No
Medio Progress in Artificial Intelligence
Alcance Article
Naturaleza Científica
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056091569&doi=10.1007%2fs13748-015-0068-7&partnerID=40&md5=1b97bb472064f148fc4728d65f2e8e49
Fecha de publicacion 01/12/2015
ISI 437872500003
Scopus Id 2-s2.0-85056091569
DOI 10.1007/s13748-015-0068-7
Abstract 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 productunit 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 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 to find a quick and reliable alternative for classifying agrarian enterprises as efficient or inefficient according to their technical efficiency. Results showed that our proposal is very promising for the classification of complex structures (farms).
Palabras clave Neural networks; Logistic regression; Classification; Product-unit; Evolutionary algorithms; Agrarian technical efficiency; Data envelopment analysis
Miembros de la Universidad Loyola

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