Combining Ranking with Traditional Methods for Ordinal Class Imbalance

• Type: Article in International Conference Proceedings Book
• Date: 2017

Authors

Cruz, R (Author) Other; Fernandes, K (Author) Other; Da Costa, JP (Author) FCUP; Ortiz, MP (Author) Other; Jaime S Cardoso (Author) FEUP

Conference proceedings International

Title: ADVANCES IN COMPUTATIONAL INTELLIGENCE, IWANN 2017, PT II

Pages: 538-548

14th International Work-Conference on Artificial Neural Networks (IWANN)

Cadiz, SPAIN, JUN 14-16, 2017

Indexing

ISI Web of Knowledge - 5 Citations

Scopus - 8 Citations

Scientific classification

FOS: Natural sciences > Mathematics

Other information

Authenticus ID: P-00M-W96

DOI: 10.1007/978-3-319-59147-6_46

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11