Olive Fly Infestation Prediction Using Machine Learning Techniques

  • José del Sagrado
  • Isabel María del Águila
Conference paper

DOI: 10.1007/978-3-540-75271-4_24

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4788)
Cite this paper as:
del Sagrado J., del Águila I.M. (2007) Olive Fly Infestation Prediction Using Machine Learning Techniques. In: Borrajo D., Castillo L., Corchado J.M. (eds) Current Topics in Artificial Intelligence. CAEPIA 2007. Lecture Notes in Computer Science, vol 4788. Springer, Berlin, Heidelberg

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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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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