Exploring the performance of resampling strategies for the class imbalance problem
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Otros documentos de la autoría: García, Vicente; Sánchez Garreta, Josep Salvador; Mollineda, Ramón A.
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Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7038
comunitat-uji-handle3:10234/8634
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Título
Exploring the performance of resampling strategies for the class imbalance problemFecha de publicación
2010Editor
Springer VerlagISSN
0302-9743Cita bibliográfica
Lecture notes in computer science (2010), vol. 6096, p. 541-549Tipo de documento
info:eu-repo/semantics/articleVersión
info:eu-repo/semantics/submittedVersionPalabras clave / Materias
Resumen
The present paper studies the influence of two distinct factors on the performance of some resampling strategies for handling imbalanced data sets. In particular, we focus on the nature of the classifier used, along ... [+]
The present paper studies the influence of two distinct factors on the performance of some resampling strategies for handling imbalanced data sets. In particular, we focus on the nature of the classifier used, along with the ratio between minority and majority classes. Experiments using eight different classifiers show that the most significant differences are for data sets with low or moderate imbalance: over-sampling clearly appears as better than under-sampling for local classifiers, whereas some under-sampling strategies outperform over-sampling when employing classifiers with global learning. [-]
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