Exploring the performance of resampling strategies for the class imbalance problem
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Altres documents de l'autoria: García, Vicente; Sánchez Garreta, Josep Salvador; Mollineda, Ramón A.
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Mostra el registre complet de l'elementcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7038
comunitat-uji-handle3:10234/8634
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Exploring the performance of resampling strategies for the class imbalance problemData de publicació
2010Editor
Springer VerlagISSN
0302-9743Cita bibliogràfica
Lecture notes in computer science (2010), vol. 6096, p. 541-549Tipus de document
info:eu-repo/semantics/articleVersió
info:eu-repo/semantics/submittedVersionParaules clau / Matèries
Resum
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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