Assessing Performance and Crossover Operators on Differential Evolution for Attribute Weighting

AudienciaPúblico en generales_ES
CoberturaMéxicoes_ES
Fecha de ingreso2026-09-15T00:29:57Z
Fecha de publicación2026-01-01
ResumenMachine learning has a wide range of applications, including classification, which categorizes elements based on their characteristics. This paper addresses the challenge of optimizing attribute weighting while assessing two crossover operators on differential evolution optimization and increasing the performance of the k-nearest neighbors classification algorithm (KNN). We use a differential evolution optimization method and assess the performance of both the differential evolution and the harmony crossover operators. Finally, the optimization method uses the accuracy of a KNN classification algorithm as a fitness function. The results show that the proposed method significantly enhances the KNN performance while proposing an alternative for other classification models such as neural networks and Random Forest.es_ES
Doihttps://doi.org/10.3390/ai7050163es_ES
URIhttps://riuat.uat.edu.mx/handle/123456789/1897
Idiomaeses_ES
EditorialMDPI AGes_ES
RelaciónAIes_ES
URL relacionadohttps://doi.org/10.3390/ai7050163es_ES
DerechosAcceso abierto (Metadatos de producción científica)es_ES
Licenciahttp://purl.org/coar/access_right/c_abf2es_ES
FuenteAI
TítuloAssessing Performance and Crossover Operators on Differential Evolution for Attribute Weightinges_ES
TipoArtículoes_ES
ArbitradoHa sido Arbitradoes_ES
AutorOrtega-Guzmán, Andrea
AutorIbarra-Martínez, Salvador
AutorCastan-Rocha, José Antonio
AutorTeran-Villanueva, J. David
AutorTreviño-Berrones, Mayra Guadalupe
AutorSantigo-Pineda, Aurelio Alejandro
AutorOrtega-Guzmán, Andreaes_ES
AutorIbarra-Martínez, Salvadores_ES
AutorCastan-Rocha, José Antonioes_ES
AutorTeran-Villanueva, J. Davides_ES
AutorTreviño-Berrones, Mayra Guadalupees_ES
AutorSantigo-Pineda, Aurelio Alejandroes_ES
InstituciónUniversidad Autónoma de Tamaulipas
InstituciónUniversidad Autónoma de Tamaulipases_ES
Número5es_ES
Rango de páginas163es_ES
URL relacionadahttps://doi.org/10.3390/ai7050163
Tipo de artículoIndexado
Tipo de artículoIndexadoes_ES
Volumen7es_ES

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