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Logistic evolutionary product-unit neural network classifier: the case of agrarian efficiency

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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).

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Notes

  1. These farms represent 59 % of Spanish agricultural land and 27 % of that in the EU. Moreover. Andalusia is the main olive-producing region in Spain yielding more than 70 % of the total production. There are whole areas devoted to the olive oil sector, which represents 30 % of Andalusian agricultural employment.

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Authors and Affiliations

  1. Department of Mathematics and Engineering, Universidad Loyola Andalucía, Córdoba, Spain

    Mercedes Torres-Jiménez, Carlos R. García-Alonso & Javier Sánchez-Monedero

  2. Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Spain

    César Hervás-Martínez

  3. Department of Financial Economics and Accounting, Universidad Loyola Andalucía, Córdoba, Spain

    Salud Millán-Lara

Authors
  1. Mercedes Torres-Jiménez
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  2. Carlos R. García-Alonso
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  3. Javier Sánchez-Monedero
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  4. Salud Millán-Lara
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  5. César Hervás-Martínez
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Corresponding author

Correspondence to Javier Sánchez-Monedero.

Additional information

This work has been partially subsidized by the TIN2014-54583-C2-1-R project of the Spanish Ministerial Commission of Science and Technology (MINECO), FEDER funds and the P2011-TIC-7508 project of the “Junta de Andalucía” (Spain).

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Torres-Jiménez, M., García-Alonso, C.R., Sánchez-Monedero, J. et al. Logistic evolutionary product-unit neural network classifier: the case of agrarian efficiency. Prog Artif Intell 4, 59–67 (2015). https://doi.org/10.1007/s13748-015-0068-7

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