Feature Selection through Filtering with Mono and Multi-Objective Memetic Algorithms Using Correlation

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AudienciaPúblico en generales_ES
CoberturaMéxicoes_ES
Fecha de ingreso2026-10-05T16:32:05Z
Fecha de publicación2025-01-01
ResumenFeature selection is the process of extracting the most relevant features from a dataset, helping to reduce its dimensionality by eliminating non-essential features. This leads to simpler, faster models and optimises training efficiency. This paper presents two memetic algorithms: one employs a mono-objective filter method as afitness function, while the other adopts a multi-objective approach. The latter uses the number of attributes in the dataset as the first objective, and the sum of Pearson's correlations for the selected attributes as the second. Additionally, we apply a novel approach to the use of correlation for attribute selection within the aforementioned memetic algorithms. Both proposals aim to identify the most relevant attributes to reduce the dimensionality of twelve test datasets. The performance of the selected features was evaluated using a J48 decision tree. The results showed a reduction in the number of attributes ranging from 14\% down to 5\%, while accuracy varied from-5\% up to 11\%, with an average improvement of over 4\% (considering only those datasets where accuracy changed).es_ES
Doihttps://doi.org/10.61467/2007.1558.2025.v16i3.856es_ES
URIhttps://riuat.uat.edu.mx/handle/123456789/3948
Idiomaenes_ES
EditorialINT JOURNAL COMBINATORIAL OPTIMIZATION PROBLEMS \& INFORMATICSes_ES
RelaciónInternational Journal of Combinatorial Optimization Problems and Informaticses_ES
URL relacionadohttps://doi.org/10.61467/2007.1558.2025.v16i3.856es_ES
DerechosAcceso abierto (Metadatos de producción científica)es_ES
Licenciahttp://purl.org/coar/access_right/c_abf2es_ES
FuenteInternational Journal of Combinatorial Optimization Problems and Informatics
Palabra claveFeature Selectiones_ES
Palabra claveMemetic Algorithmes_ES
Palabra claveNSGA-IIes_ES
TítuloFeature Selection through Filtering with Mono and Multi-Objective Memetic Algorithms Using Correlationes_ES
TipoArtículoes_ES
ArbitradoHa sido Arbitradoes_ES
AutorZamarron-Escobar, Daniel E.
AutorTeran-Villanueva, Jesus D.
AutorIbarra-Martinez, Salvador
AutorSantiago-Pineda, Aurelio A.
AutorZamarron-Escobar, Daniel E.es_ES
AutorTeran-Villanueva, Jesus D.es_ES
AutorIbarra-Martinez, Salvadores_ES
AutorSantiago-Pineda, Aurelio A.es_ES
InstituciónUniversidad Autónoma de Tamaulipas
InstituciónUniversidad Autónoma de Tamaulipases_ES
Número3es_ES
Rango de páginas512-528es_ES
URL relacionadahttps://doi.org/10.61467/2007.1558.2025.v16i3.856
Tipo de artículoIndexado
Tipo de artículoIndexadoes_ES
Volumen16es_ES

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