Ordinal Class Imbalance with Ranking

Research areas:

• Methodology - Machine learning

Year:

2017

Type of Publication:

In Proceedings

Authors:

• Cruz, R.
• Fernandes, K.
• Costa, J. F. Pinto
• Pérez-Ortiz, María
• Cardoso, J. S.

Volume:

10255

Book title:

Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2017)

Series:

Lecture Notes in Computer Science

Pages:

3-12

ISBN:

978-3-319-58837-7

BibTex:

@conference{R. Cruz20171, author = "R. Cruz and K. Fernandes and J.F. Pinto Costa and Mar{\'i}a P{\'e}rez-Ortiz and J. S. Cardoso", 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.", booktitle = "Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2017)", doi = " 10.1007/978-3-319-58838-4_1", isbn = "978-3-319-58837-7", pages = "3-12", series = "Lecture Notes in Computer Science", title = "{O}rdinal {C}lass {I}mbalance with {R}anking", url = "http://dx.doi.org/10.1007/978-3-319-58838-4_1", volume = "10255", year = "2017", }

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.