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Exploring the performance of resampling strategies for the class imbalance problem
dc.contributor.author | García, Vicente | |
dc.contributor.author | Sánchez Garreta, Josep Salvador | |
dc.contributor.author | Mollineda, Ramón A. | |
dc.date.accessioned | 2012-02-16T09:24:16Z | |
dc.date.available | 2012-02-16T09:24:16Z | |
dc.date.issued | 2010 | |
dc.identifier.citation | Lecture notes in computer science (2010), vol. 6096, p. 541-549 | |
dc.identifier.issn | 0302-9743 | |
dc.identifier.uri | http://hdl.handle.net/10234/31856 | |
dc.description.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 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. | |
dc.format.extent | 9 p. | |
dc.format.mimetype | application/pdf | |
dc.language.iso | eng | |
dc.publisher | Springer Verlag | |
dc.relation.isFormatOf | Versió pre-print del document publicat a: http://www.springerlink.com/content/r462422v128q5335/ | |
dc.rights.uri | http://rightsstatements.org/vocab/CNE/1.0/ | * |
dc.subject | Resampling strategies | |
dc.subject | Class imbalance | |
dc.title | Exploring the performance of resampling strategies for the class imbalance problem | |
dc.type | info:eu-repo/semantics/article | |
dc.rights.holder | © Springer Verlag | |
dc.identifier.doi | http://dx.doi.org/10.1007/978-3-642-13022-9_54 | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
dc.type.version | info:eu-repo/semantics/submittedVersion |
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