Título Ordinal Class Imbalance with Ranking
Autores Cruz, Ricardo , Fernandes, Kelwin , Pinto Costa, Joaquim F. , PÉREZ ORTIZ, MARÍA, Cardoso, Jaime S.
Publicación externa No
Medio Lecture Notes in Computer Science
Alcance Proceedings Paper
Naturaleza Científica
Cuartil JCR 4
Cuartil SJR 2
Impacto SJR 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
Fecha de publicacion 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.
Palabras clave Ordinal classification; Class imbalance; Ranking; SVM
Miembros de la Universidad Loyola

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