Cost-sensitive learning for Rule Classification: Evaluation of its applicability for Integrated Pest Management

Authors

  • Borja Espejo-García Advanced Information Systems group (IAAA) Instituto de Investigación en Ingeniería de Aragón (I3A) Universidad de Zaragoza
  • Francisco Javier López-Pellicer Advanced Information Systems group (IAAA) Instituto de Investigación en Ingeniería de Aragón (I3A) Universidad de Zaragoza
  • Francisco Javier Zarazaga-Soria Advanced Information Systems group (IAAA) Instituto de Investigación en Ingeniería de Aragón (I3A) Universidad de Zaragoza

DOI:

https://doi.org/10.26754/jji-i3a.201701617

Abstract

This work evaluates and compares different supervised learning algorithms using a costsensitive approach to find a model that classifies legal rules related to pesticides as prohibitions and permissions. The naive Bayes classifier achieves the best results and it would be applicable because it doesn't misclassify prohibitions as permissions.

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How to Cite

Espejo-García, B., López-Pellicer, F. J., & Zarazaga-Soria, F. J. (2017). Cost-sensitive learning for Rule Classification: Evaluation of its applicability for Integrated Pest Management. Jornada De Jóvenes Investigadores Del I3A, 4, 57–58. https://doi.org/10.26754/jji-i3a.201701617

Issue

Vol. 4: Actas de la V Jornada de Jóvenes Investigadores del I3A - 27 de mayo de 2016

Section

Artículos (Tecnologías de la Información y las Comunicaciones)