Combining ranking with traditional methods for ordinal class imbalance

dc.contributor.author Cruz,R en
dc.contributor.author Kelwin Alexander Correia en
dc.contributor.author Pinto Costa,JF en
dc.contributor.author Ortiz,MP en
dc.contributor.author Jaime Cardoso en
dc.date.accessioned 2018-01-21T15:52:13Z
dc.date.available 2018-01-21T15:52:13Z
dc.date.issued 2017 en
dc.description.abstract In classification problems, a dataset is said to be imbalanced when the distribution of the target variable is very unequal. Classes contribute unequally to the decision boundary, and special metrics are used to evaluate these datasets. In previous work, we presented pairwise ranking as a method for binary imbalanced classification, and extended to the ordinal case using weights. In this work, we extend ordinal classification using traditional balancing methods. A comparison is made against traditional and ordinal SVMs, in which the ranking adaption proposed is found to be competitive. © Springer International Publishing AG 2017. en
dc.identifier.uri http://repositorio.inesctec.pt/handle/123456789/7175
dc.identifier.uri http://dx.doi.org/10.1007/978-3-319-59147-6_46 en
dc.language eng en
dc.relation 5958 en
dc.relation 3889 en
dc.rights info:eu-repo/semantics/openAccess en
dc.title Combining ranking with traditional methods for ordinal class imbalance en
dc.type conferenceObject en
dc.type Publication en
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