| Title |
Evolutionary Product Unit Logistic Regression: The Case of Agrarian Efficiency |
| Authors |
GARCÍA ALONSO, CARLOS, Hervas-Martinez, Cesar , MILLÁN LARA, SALUD, TORRES JIMÉNEZ, MERCEDES |
| External publication |
No |
| Means |
Lect. Notes Comput. Sci. |
| Scope |
Proceedings Paper |
| Nature |
Científica |
| JCR Quartile |
4 |
| SJR Quartile |
2 |
| SJR Impact |
0.36900 |
| Web |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84952673352&doi=10.1007%2f978-3-319-24598-0_9&partnerID=40&md5=58176bfba7ceb691cac7abdb06b1f8be |
| Publication date |
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). |
| Keywords |
Neural networks; Classification; Product-Unit; Evolutionary algorithms; Agrarian technical efficiency |
| Universidad Loyola members |
|
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