Please use this identifier to cite or link to this item: http://hdl.handle.net/10077/4002
DC FieldValueLanguage
dc.contributor.authorMenardi, Giovanna-
dc.contributor.authorTorelli, Nicola-
dc.date.accessioned2011-02-17T09:33:32Z-
dc.date.available2011-02-17T09:33:32Z-
dc.date.issued2010-
dc.identifier.citationGiovanna Menardi, Nicola Torelli, "Training and assessing classification rules with unbalanced data", Working Paper Series, N. 2, 2010.it_IT
dc.identifier.isbn978-88-8303-321-6-
dc.identifier.urihttp://hdl.handle.net/10077/4002-
dc.description.abstractThe problem of modeling binary responses by using cross-sectional data has been addressed with a number of satisfying solutions that draw on both parametric and nonparametric methods. However, there exist many real situations where one of the two responses (usually the most interesting for the analysis) is rare. It has been largely reported that this class imbalance heavily compromises the process of learning, because the model tends to focus on the prevalent class and to ignore the rare events. However, not only the estimation of the classification model is affected by a skewed distribution of the classes, but also the evaluation of its accuracy is jeopardized, because the scarcity of data leads to poor estimates of the model’s accuracy. In this work, the effects of class imbalance on model training and model assessing are discussed. Moreover, a unified and systematic framework for dealing with both the problems is proposed, based on a smoothed bootstrap re-sampling technique.it_IT
dc.language.isoenit_IT
dc.publisherEUT Edizioni Università di Trieste-
dc.relation.ispartofseriesWorking paper series - Dipartimento di scienze economiche, aziendali, matematiche e statistiche "Bruno de Finetti"it_IT
dc.relation.ispartofseries2 (2010)it_IT
dc.subjectaccuracyit_IT
dc.subjectbinary classificationit_IT
dc.subjectbootstrapit_IT
dc.subjectkernel density estimationit_IT
dc.subjectunbalanced learningit_IT
dc.titleTraining and assessing classification rules with unbalanced datait_IT
dc.typeBook Chapter-
item.languageiso639-1en-
item.openairetypebookPart-
item.grantfulltextopen-
item.fulltextWith Fulltext-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_3248-
Appears in Collections:Working Papers Series 2010, 2
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