GARCÍA ALONSO, CARLOS, Hervas-Martinez, Cesar , MILLÁN LARA, SALUD, TORRES JIMÉNEZ, MERCEDES
No
Lect. Notes Comput. Sci.
Proceedings Paper
Científica
0.369
01/01/2015
000367709100009
2-s2.0-84952673352
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).
Neural networks; Classification; Product-Unit; Evolutionary algorithms; Agrarian technical efficiency