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Fast feature selection aimed at high-dimensional data via hybrid-sequential-ranked searches

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Autor: Ruíz, Roberto
Riquelme Santos, José Cristóbal
García Torres, M.
Departamento: Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Fecha: 2012
Publicado en: Expert Systems with Applications, 39 (12), 11094-11102.
Tipo de documento: Artículo
Resumen: We address the feature subset selection problem for classification tasks. We examine the performance of two hybrid strategies that directly search on a ranked list of features and compare them with two widely used algorithms, the fast correlation based filter (FCBF) and sequential forward selection (SFS). The pro-posed hybrid approaches provide the possibility of efficiently applying any subset evaluator, with a wrap-per model included, to large and high-dimensional domains. The experiments performed show that our two strategies are competitive and can select a small subset of features without degrading the classifica-tion error or the advantages of the strategies under study.
Cita: Ruíz, R., Riquelme Santos, J.C. y García Torres, M. (2012). Fast feature selection aimed at high-dimensional data via hybrid-sequential-ranked searches. Expert Systems with Applications, 39 (12), 11094-11102.
Tamaño: 562.4Kb
Formato: PDF

URI: http://hdl.handle.net/11441/43498

DOI: http://dx.doi.org/10.1016/j.eswa.2012.03.061

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