A comparative study between feature selection algorithms
Cargando...
Fecha
Director
Enlace al recurso
ORCID
Google Scholar
Cvlac
gruplac
Descripción Dominio:
Título de la revista
ISSN de la revista
Título del volumen
Editor
Compartir
Documentos PDF
Descripción
Abstract
In this paper, we show a comparative study between four algorithms
used in features selection; these are: decision trees, entropy measure for ranking
features, estimation of distribution algorithms, and the bootstrapping algorithm.
Likewise, the features selection is highlighted as the most representative task in
the elimination of noise, in order to improve the quality of the dataset. Subsequently,
each algorithm is described in order that the reader understands its
function. Then the algorithms are applied using different data sets and obtaining
the results in the selection. Finally, the conclusions of this investigation are
presented.
Idioma
Palabras clave
Citación
Medina Garcia, V. H., Rodriguez Rodriguez, J., & Ospina Usaquén, M. A. (2018). A comparative study between feature selection algorithms. Bogotá: doi:10.1007/978-3-319-93803-5_7
Colecciones
Licencia Creative Commons
Atribución-NoComercial-CompartirIgual 2.5 Colombia