Skip to main content
SpringerLink
Log in

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

  • Regular Paper
  • Published:
Progress in Artificial Intelligence Aims and scope Submit manuscript

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).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

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.

References

  1. Banker, R.D., Cooper, W.W., Seiford, L.M., Thrall, R.M., Zhu, J.: Returns to scale in different DEA models. Eur. J. Oper. Res. 154(2), 345–362 (2004). (DEA and its uses in different countries)

    Article  MathSciNet  MATH  Google Scholar 

  2. Charnes, A., Cooper, W., Rhodes, E.: Measuring the efficiency of decision making units. Eur. J. Oper. Res. 2(6), 429–444 (1978)

    Article  MathSciNet  MATH  Google Scholar 

  3. Consejería de Economía y Hacienda de la Junta de Andalucía: Sistema de Información Multiterritorial de Andalucía, Instituto de Estadística de Andalucía (2005). http://www.juntadeandalucia.es/institutodeestadisticaycartografia/sima/

  4. Cooper, W., Seiford, L., Zhu, J.: Data envelopment analysis. In: Cooper, W., Seiford, L., Zhu, J. (eds.) Handbook on Data Envelopment Analysis. International Series in Operations Research & Management Science, vol. 71, pp. 1–39. Springer, US (2004)

    Chapter  Google Scholar 

  5. de Andrés, J., Landajo, M., Lorca, P.: Forecasting business profitability by using classification techniques: a comparative analysis based on a Spanish case. Eur. J. Oper. Res. 167(2), 518–542 (2005)

    Article  MATH  Google Scholar 

  6. Durbin, R., Rumelhart, D.: Products units: a computationally powerful and biologically plausible extension to backpropagation networks. Neural Comput. 1(1), 133–142 (1989)

    Article  Google Scholar 

  7. Freund, Y., Mason, L.: The alternating decision tree learning algorithm. In: Proceedings of the Sixteenth International Conference on Machine Learning, ICML ’99, pp. 124–133. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999)

  8. Freund, Y., Schapire, R.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  9. García, C., Marín, H.: An integrated model to study the structural and financial sustainability of agricultural enterprises. Acta Hortic. 674, 313–320 (2005)

    Article  Google Scholar 

  10. Gutiérrez, P.A., Hervás-Martínez, C., Martínez-Estudillo, F.J.: Logistic regression by means of evolutionary radial basis function neural networks. IEEE Trans. Neural Netw. 22(2), 246–263 (2011)

    Article  Google Scholar 

  11. Gutiérrez, P., Segovia-Vargas, M., Salcedo-Sanz, S., Hervás-Martínez, C., Sanchis, A., Portilla-Figueras, J., Fernández-Navarro, F.: Hybridizing logistic regression with product unit and RBF networks for accurate detection and prediction of banking crises. Omega 38(5), 333–344 (2010). (Cited By 7)

    Article  Google Scholar 

  12. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. Spec. Interest Group Knowl. Discov. Data Min. Explor. Newslett. 11, 10–18 (2009)

    Google Scholar 

  13. Haykin, S.: Neural Networks: A Comprehensive Foundation, 3rd edn. Prentice Hall, Upper Saddle River (2008)

  14. Japkowicz, N., Stephen, S.: The class imbalance problem: a systematic study. Intell. Data Anal. J. 6(5), 429–450 (2002)

    MATH  Google Scholar 

  15. Johnson, R.A., Wichern, D.W. (eds.): Applied Multivariate Statistical Analysis. Prentice-Hall Inc, Upper Saddle River (1988)

  16. Kibler, D.F., Aha, D.W., Albert, M.K.: Instance-based prediction of real-valued attributes. Comput. Intell. 5, 51 (1989)

    Article  Google Scholar 

  17. Landwehr, N., Hall, M., Frank, E.: Logistic model trees. Mach. Learn. 59(1–2), 161–205 (2005)

    Article  MATH  Google Scholar 

  18. López, V., Fernández, A., García, S., Palade, V., Herrera, F.: An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics. Inf. Sci. 250, 113–141 (2013)

    Article  Google Scholar 

  19. MacFarlane, R.: Modelling the interaction of economic and socio-behavioural factors in the prediction of farm adjustment. J. Rural Stud. 12(4), 365–374 (1996)

    Article  Google Scholar 

  20. Martínez-Estudillo, A.C., Martínez-Estudillo, F.J., Hervás-Martínez, C., García, N.: Evolutionary product unit based neural networks for regression. Neural Netw. 19(4), 477–486 (2006)

    Article  MATH  Google Scholar 

  21. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, USA (1993)

    Google Scholar 

  22. Sadras, V., Roget, D., Krause, M.: Dynamic cropping strategies for risk management in dry-land farming systems. Agric. Syst. 76(3), 929–948 (2003)

    Article  Google Scholar 

  23. Torres, M., Hervás, C., García, C.: Multinomial logistic regression and product unit neural network models: application of a new hybrid methodology for solving a classification problem in the livestock sector. Expert Syst. Appl. 36(10), 12225–12235 (2009)

    Article  Google Scholar 

  24. Webster, J.: Assessing the economic consequences of sustainability in agriculture. Agric. Ecosyst. Environ. 64(2), 95–102 (1997)

    Article  Google Scholar 

Download references

Author information

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Carlos R. García-Alonso
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Javier Sánchez-Monedero
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Salud Millán-Lara
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. César Hervás-Martínez
    View author publications

    You can also search for this author in PubMed Google Scholar

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).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13748-015-0068-7

Keywords

Search

Navigation