Discretization Techniques and Genetic Algorithm for Learning the Classification Method PROAFTN

Authors:
Feras Al-Obeidat;Nabil Belacel;Prabhat Mahanti;Juan A. Carretero
Affiliations:
-;-;-;-
Venue:
ICMLA '09 Proceedings of the 2009 International Conference on Machine Learning and Applications
Year:
2009

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Automatic parameter settings for the PROAFTN classifier using hybrid particle swarm optimization

AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence

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Abstract

This paper introduces new techniques for learning the classification method PROAFTN from data. PROAFTN is a multi-criteria classification method and belongs to the class of supervised learning algorithms. To use PROAFTN for classification, some parameters must be obtained for this purpose. Therefore, an automatic method to extract these parameters from data with minimum classification errors is required. Here, discretization techniques and genetic algorithms are proposed for establishing these parameters and then building the classification model. Based on the obtained results, the newly proposed approach outperforms widely used classification methods.