Name
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-24598-0_9 | CAEPIA |
Keywords | Field | DocType |
Neural networks,Classification,Product-Unit,Evolutionary algorithms,Agrarian technical efficiency | Econometrics,Evolutionary algorithm,Agriculture,Variables,Data envelopment analysis,Artificial neural network,Logistic regression,Agrarian society,Geography | Conference |
Volume | ISSN | Citations |
9422 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carlos R. García-Alonso | 1 | 244 | 10.72 |
| César Hervás-Martínez | 2 | 796 | 78.92 |
| Salud Millán-Lara | 3 | 0 | 0.34 |
| Mercedes Torres-Jiménez | 4 | 19 | 2.76 |