Exploring the performance of resampling strategies for the class imbalance problem
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Other documents of the author: García, Vicente; Sánchez Garreta, Josep Salvador; Mollineda, Ramón A.
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Show full item recordcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7038
comunitat-uji-handle3:10234/8634
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INVESTIGACIONMetadata
Title
Exploring the performance of resampling strategies for the class imbalance problemDate
2010Publisher
Springer VerlagISSN
0302-9743Bibliographic citation
Lecture notes in computer science (2010), vol. 6096, p. 541-549Type
info:eu-repo/semantics/articleVersion
info:eu-repo/semantics/submittedVersionSubject
Abstract
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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