Associative learning on imbalanced environments: An empirical study
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Otros documentos de la autoría: Cleofás Sánchez, Laura; Sánchez Garreta, Josep Salvador; García, V.; Valdovinos Rosas, Rosa María
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Título
Associative learning on imbalanced environments: An empirical studyAutoría
Fecha de publicación
2015-10xmlui.dri2xhtml.METS-1.0.item-edition
PreprintEditor
ElsevierCita bibliográfica
CLEOFAS-SÁNCHEZ, L., et al. Associative learning on imbalanced environments: An empirical study. Expert Systems with Applications, 2015.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
http://www.sciencedirect.com/science/article/pii/S0957417415006880Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Associative memories have emerged as a powerful computational neural network model for several pattern classification problems. Like most traditional classifiers, these models assume that the classes share similar ... [+]
Associative memories have emerged as a powerful computational neural network model for several pattern classification problems. Like most traditional classifiers, these models assume that the classes share similar prior probabilities. However, in many real-life applications the ratios of prior probabilities between classes are extremely skewed. Although the literature has provided numerous studies that examine the performance degradation of renowned classifiers on different imbalanced scenarios, so far this effect has not been supported by a thorough empirical study in the context of associative memories. In this paper, we fix our attention on the applicability of the associative neural networks to the classification of imbalanced data. The key questions here addressed are whether these models perform better, the same or worse than other popular classifiers, how the level of imbalance affects their performance, and whether distinct resampling strategies produce a different impact on the associative memories. In order to answer these questions and gain further insight into the feasibility and efficiency of the associative memories, a large-scale experimental evaluation with 31 databases, seven classification models and four resampling algorithms is carried out here, along with a non-parametric statistical test to discover any significant differences between each pair of classifiers. [-]
Publicado en
Expert Systems with Applications Volume 54, 15 July 2016Derechos de acceso
info:eu-repo/semantics/openAccess
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