Discretization Techniques and Genetic Algorithm for Learning the Classification Method PROAFTN

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...

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Bibliographic Details
Main Authors: Al-Obeidat, F., Belacel, N., Mahanti, P., Carretero, J.A.
Format: Conference Proceeding
Language:English
Subjects:
Application software
Computer science
Delta modulation
Genetic algorithms
Information technology
Machine learning
Nearest neighbor searches
Niobium
Prototypes
Supervised learning
Online Access:Request full text
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Summary: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.
DOI:10.1109/ICMLA.2009.37