Olive fly infestation prediction using automatic learning techniques

• Sagrado, José del ^[1] ; Águila, Isabel María del ^[1]
  1. [1] Universidad de Almería

     Universidad de Almería

     Almería, España

     
• Localización: XII Conferencia de la Asociación Española para la Inteligencia Artificial: (CAEPIA 2007). Actas / coord. por Daniel Borrajo Millán , Luis Castillo Vidal , Juan Manuel Corchado Rodríguez , Vol. 1, 2007, ISBN 978-84-611-8847-5, págs. 267-276
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • This article reports on a study on olive-fly infestation prediction using automatic 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 Bayesian networks produce valid predictors, although improved definition of dependencies and refinement of the variables selection methods are required.