Robust weighted general performance score for various classification scenarios

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AudienciaPúblico en generales_ES
CoberturaMéxicoes_ES
Fecha de ingreso2026-10-05T16:33:18Z
Fecha de publicación2024-01-01
ResumenTraditionally, performance measures such as accuracy, recall, precision, specificity, and negative predicted value (NPV) have been used to evaluate a classification model's performance. However, these measures often fall short of capturing different classification scenarios, such as binary or multi-class, balanced or imbalanced, and noisy or noiseless data. Therefore, there is a need for a robust evaluation metric that can assist business decision-makers in selecting the most suitable model for a given scenario. Recently, a general performance score (GPS) comprising different combinations of traditional performance measures (TPMs) was proposed. However, it indiscriminately assigns equal importance to each measure, often leading to inconsistencies. To overcome the shortcomings of GPS, we introduce an enhanced metric called the Weighted General Performance Score (W-GPS) that considers each measure's coefficient of variation (CV) and subsequently assigns weights to that measure based on its CV value. Considering consistency as a criterion, we found that W-GPS outperformed GPS in the above-mentioned classification scenarios. Further, considering W-GPS with different weighted combinations of TPMs, it was observed that no demarcation of these combinations that work best in a given scenario exists. Thus, W-GPS offers flexibility to the user to choose the most suitable combination for a given scenario.es_ES
Doihttps://doi.org/10.3233/idt-240465es_ES
URIhttps://riuat.uat.edu.mx/handle/123456789/5107
Idiomaenes_ES
EditorialSAGE PUBLICATIONS INCes_ES
RelaciónIntelligent Decision Technologieses_ES
URL relacionadohttps://doi.org/10.3233/idt-240465es_ES
DerechosAcceso restringido / Suscripción (Metadatos de producción científica)es_ES
Licenciahttp://purl.org/coar/access_right/c_16eces_ES
FuenteIntelligent Decision Technologies
Palabra clavePerformance measureses_ES
Palabra claveclassificationes_ES
Palabra claveimbalanced data modellinges_ES
Palabra clavenoisy data modellinges_ES
Palabra clavecoefficient of variationes_ES
TítuloRobust weighted general performance score for various classification scenarioses_ES
TipoArtículoes_ES
ArbitradoHa sido Arbitradoes_ES
AutorPandey, Gaurav
AutorBagri, Rashika
AutorGupta, Rajan
AutorRajpal, Ankit
AutorAgarwal, Manoj
AutorKumar, Naveen
AutorPandey, Gauraves_ES
AutorBagri, Rashikaes_ES
AutorGupta, Rajanes_ES
AutorRajpal, Ankites_ES
AutorAgarwal, Manojes_ES
AutorKumar, Naveenes_ES
InstituciónUniversidad Autónoma de Tamaulipas
InstituciónUniversidad Autónoma de Tamaulipases_ES
Número3es_ES
Rango de páginas2033-2054es_ES
URL relacionadahttps://doi.org/10.3233/idt-240465
Tipo de artículoIndexado
Tipo de artículoIndexadoes_ES
Volumen18es_ES

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