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Title: Combining ranking with traditional methods for ordinal class imbalance
Authors: Cruz,R
Kelwin Alexander Correia
Pinto Costa,JF
Jaime Cardoso
Issue Date: 2017
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.
metadata.dc.type: conferenceObject
Appears in Collections:CTM - Articles in International Conferences

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