Title Ordinal Class Imbalance with Ranking
Authors Cruz, Ricardo , Fernandes, Kelwin , Pinto Costa, Joaquim F. , PÉREZ ORTIZ, MARÍA, Cardoso, Jaime S.
External publication No
Means Lect. Notes Comput. Sci.
Scope Proceedings Paper
Nature Científica
JCR Quartile 4
SJR Quartile 2
SJR Impact 0.295
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021222194&doi=10.1007%2f978-3-319-58838-4_1&partnerID=40&md5=9206d3cbef8e420ecc4b0bab052ecae6
Publication date 01/01/2017
ISI 000429969200001
Scopus Id 2-s2.0-85021222194
DOI 10.1007/978-3-319-58838-4_1
Abstract Classification datasets, which feature a skewed class distribution, are said to be class imbalance. Traditional methods favor the larger classes. We propose pairwise ranking as a method for imbalance classification so that learning compares pairs of observations from each class, and therefore both contribute equally to the decision boundary. In previous work, we suggested treating the binary classification as a ranking problem, followed by a threshold mapping to convert back the ranking score to the original classes. In this work, the method is extended to multi-class ordinal classification, and a new mapping threshold is proposed. Results are compared with traditional and ordinal SVMs, and ranking obtains competitive results.
Keywords Ordinal classification; Class imbalance; Ranking; SVM
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