Artículo
Fast feature selection aimed at high-dimensional data via hybrid-sequential-ranked searches
Autor/es | Ruiz, Roberto
Riquelme Santos, José Cristóbal García Torres, M. |
Departamento | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Fecha de publicación | 2012 |
Fecha de depósito | 2016-07-12 |
Publicado en |
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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, ... 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. |
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Fast festure selection aimed.pdf | 562.4Kb | [PDF] | Ver/ | |