Discretization Techniques and Genetic Algorithm for Learning the Classification method PROAFTN

DOIResolve DOI: http://doi.org/10.1109/ICMLA.2009.37
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Proceedings titleProceedings of the Eighth International Conference On Machine Learning and Applications, 2009 (ICMLA'09)
ConferenceEighth International Conference On Machine Learning and Applications (IEEE Computer Society), Miami, Florida, December 13–15, 2009
Pages685688; # of pages: 4
SubjectInformation and Communications Technologies
AbstractThis 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.
Publication date
PublisherIEEE Computer Society
AffiliationNational Research Council Canada (NRC-CNRC)
Peer reviewedYes
NPARC number15261135
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Record identifier1ef60dc2-ec9d-4a91-9ea8-72681838d195
Record created2010-06-10
Record modified2016-05-09
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