On the effectiveness of preprocessing methods when dealing with different levels of class imbalance
Impacto
Scholar |
Otros documentos de la autoría: Sánchez Garreta, Josep Salvador; García, Vicente; Mollineda, Ramón A.
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Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/43662
comunitat-uji-handle3:10234/43643
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http://dx.doi.org/10.1016/j.knosys.2011.06.013 |
Metadatos
Título
On the effectiveness of preprocessing methods when dealing with different levels of class imbalanceFecha de publicación
2012Editor
ElsevierISSN
0950-7051Cita bibliográfica
Knowledge-Based Systems (Feb. 2012) vol. 25, no. 1, p. 13-21Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
http://www.sciencedirect.com/science/article/pii/S0950705111001286Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
The present paper investigates the influence of both the imbalance ratio and the classifier on the performance of several resampling strategies to deal with imbalanced data sets. The study focuses on evaluating how ... [+]
The present paper investigates the influence of both the imbalance ratio and the classifier on the performance of several resampling strategies to deal with imbalanced data sets. The study focuses on evaluating how learning is affected when different resampling algorithms transform the originally imbalanced data into artificially balanced class distributions. Experiments over 17 real data sets using eight different classifiers, four resampling algorithms and four performance evaluation measures show that over-sampling the minority class consistently outperforms under-sampling the majority class when data sets are strongly imbalanced, whereas there are not significant differences for databases with a low imbalance. Results also indicate that the classifier has a very poor influence on the effectiveness of the resampling strategies [-]
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