Olive Fly Infestation Prediction Using Machine Learning Techniques

  • José del Sagrado
  • Isabel María del Águila
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4788)

Abstract

This article reports on a study on olive-fly infestation prediction using machine learning techniques. . The purpose of the work was, on the one hand, to make accurate predictions and, on the other, to verify whether the Bayesian network techniques are competitive with respect to classification trees. We have applied the techniques to a dataset and, in addition, performed a previous phase of variables selection to simplify the complexity of the classifiers. The results of the experiments show that Bayesians networks produce valid predictors, although improved definition of dependencies and refinement of the variables selection methods are required.

Keywords

Data mining Bayesian Networks Knowledge Based Systems Integrated Production 

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References

  1. 1.
    Águila, I.M., Cañadas, J., Bosch, A., Túnez, S., Marín, R.: Knowledge model of therapy administration task applied to an agricultural domain. In: Palade, V., Howlett, R.J., Jain, L. (eds.) KES 2003. LNCS, vol. 2774, pp. 1277–1283. Springer, Heidelberg (2003)Google Scholar
  2. 2.
    Duda, R.O., Hart, P.E.: Pattern classification. John Wiley and Sons, New York (2001)MATHGoogle Scholar
  3. 3.
    Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian networks classifiers. Machine Learrning 29, 131–163 (1997)MATHCrossRefGoogle Scholar
  4. 4.
    Jensen, F.V.: Bayesian Networks and decision graphs. Springer, Heidelberg (2001)MATHGoogle Scholar
  5. 5.
    Kristensen, K., Rasmussen, I.A.: The use of a Bayesian network in the design of a decision support system for growing malting barley without use of pesticides. Computers and Electronics in Agriculture 33, 197–217 (2002)CrossRefGoogle Scholar
  6. 6.
    Maman, B.D., Harizanis, P., Filis, I., Antonopoulou, E., Yialouris, C.P., Sideridis, A.B.: A diagnostic expert system for honeybee pests. Computers and Electronics in Agriculture 36, 17–31 (2002)CrossRefGoogle Scholar
  7. 7.
    de Melo, A.C.V., Sanchez, A.J.: Software maintenance project delays prediction using Bayesian Networks. Expert Systems with Applications (2007), doi:10.1016/j.eswa.2006.10.040Google Scholar
  8. 8.
    Lauría, E.J., Duchessi, P.J.: A Bayesian Belief Network for IT implementation decision support. Decision Support Systems 42, 1573–1588 (2006)CrossRefGoogle Scholar
  9. 9.
    Perini, A., Susi, A.: Developing a decision support system for integrated production in Agriculture. Environmental Modelling & Software 19, 821–829 (2004)CrossRefGoogle Scholar
  10. 10.
    Sahami, M.: Learning limited dependence Bayesian classifiers. In: Proceedings of Second internacional Coference of Knowledge Discovery and Data Mining, pp. 335–338 (2002)Google Scholar
  11. 11.
    Túnez, S., Aguila, I., Marín, M.R.: An Expertise Model for Therapy Planning Using Abductive Reasoning. Cybernetics and Systems: An international Journal 32, 829–849 (2001)MATHCrossRefGoogle Scholar
  12. 12.
    Zhu, J.Y., Deshmukh, A.: Application of Bayesian decision networks to life cycle engineering in Green design and manufacturing Engineering. Applications of Artificial Intelligence 16, 91–103 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • José del Sagrado
    • 1
  • Isabel María del Águila
    • 1
  1. 1.Dpt. of Languages and Computation, University of Almería, 04120 AlmeríaSpain

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