Graph-based approaches for over-sampling in the context of ordinal regression

María PÉREZ-ORTIZ, Pedro Antonio GUTIÉRREZ, César HERVÁS-MARTÍNEZ, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

52 Citations (Scopus)

Abstract

The classification of patterns into naturally ordered labels is referred to as ordinal regression or ordinal classification. Usually, this classification setting is by nature highly imbalanced, because there are classes in the problem that are a priori more probable than others. Although standard over-sampling methods can improve the classification of minority classes in ordinal classification, they tend to introduce severe errors in terms of the ordinal label scale, given that they do not take the ordering into account. A specific ordinal over-sampling method is developed in this paper for the first time in order to improve the performance of machine learning classifiers. The method proposed includes ordinal information by approaching over-sampling from a graph-based perspective. The results presented in this paper show the good synergy of a popular ordinal regression method (a reformulation of support vector machines) with the graph-based proposed algorithms, and the possibility of improving both the classification and the ordering of minority classes. A cost-sensitive version of the ordinal regression method is also introduced and compared with the over-sampling proposals, showing in general lower performance for minority classes. © 2014 IEEE.
Original languageEnglish
Article number6940273
Pages (from-to)1233-1245
Number of pages13
JournalIEEE Transactions on Knowledge and Data Engineering
Volume27
Issue number5
Early online date30 Oct 2014
DOIs
Publication statusPublished - 1 May 2015
Externally publishedYes

Keywords

  • imbalanced classification
  • ordinal classification
  • ordinal regression
  • Over-sampling

Fingerprint

Dive into the research topics of 'Graph-based approaches for over-sampling in the context of ordinal regression'. Together they form a unique fingerprint.

Cite this