Paper Info
Title
Logistic evolutionary product-unit neural network classifier: the case of agrarian efficiency.
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).
Year
DOI
Venue
2015
10.1007/s13748-015-0068-7
Progress in AI
Keywords
Field
DocType
Neural networks, Logistic regression, Classification, Product-unit, Evolutionary algorithms, Agrarian technical efficiency, Data envelopment analysis
Data mining,Evolutionary algorithm,Neural network classifier,Computer science,Data envelopment analysis,Artificial intelligence,Variables,Artificial neural network,Agrarian society,Logistic regression,Machine learning,Returns to scale
Journal
Volume
Issue
ISSN
4
3-4
2192-6360
Citations 
PageRank 
References 
0
0.34
12
Authors
5
Name
Order
Citations
PageRank
Mercedes Torres-Jiménez1192.76
Carlos R. García-Alonso224410.72
Javier Sánchez-Monedero39812.18
Salud Millán-Lara400.34
César Hervás-Martínez579678.92