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2017 | OriginalPaper | Chapter

Ordinal Class Imbalance with Ranking

Authors : Ricardo Cruz, Kelwin Fernandes, Joaquim F. Pinto Costa, María Pérez Ortiz, Jaime S. Cardoso

Published in: Pattern Recognition and Image Analysis

Publisher: Springer International Publishing

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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.

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Footnotes
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Metadata
Title
Ordinal Class Imbalance with Ranking
Authors
Ricardo Cruz
Kelwin Fernandes
Joaquim F. Pinto Costa
María Pérez Ortiz
Jaime S. Cardoso
Copyright Year
2017
Book
Pattern Recognition and Image Analysis

Print ISBN: 978-3-319-58837-7

Electronic ISBN: 978-3-319-58838-4

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-58838-4_1

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