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.
CITATION STYLE
Cruz, R., Fernandes, K., Pinto Costa, J. F., Ortiz, M. P., & Cardoso, J. S. (2017). Combining ranking with traditional methods for ordinal class imbalance. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10306 LNCS, pp. 538–548). Springer Verlag. https://doi.org/10.1007/978-3-319-59147-6_46
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