Graph-Based Approaches for Over-sampling in the context of Ordinal Regression

Research areas:

• Methodology - Machine learning

Year:

2015

Type of Publication:

Article

Keywords:

Over-sampling, imbalanced classification, ordinal regression, ordinal classification

Authors:

• Pérez-Ortiz, María
• Gutiérrez, Pedro Antonio
• Hervás-Martínez, César
• Yao, X.

Journal:

IEEE Transactions on Knowledge and Data Engineering

Volume:

27

Number:

5

Pages:

1233-1245

Month:

May

ISSN:

1041-4347

BibTex:

@article{TKDE2015Maria, author = "Mar{\'i}a P{\'e}rez-Ortiz and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and X. Yao", 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. ", awards = "JCR(2015): 2.476 Position: 17/143 (Q1) Category: COMPUTER SCIENCE, INFORMATION SYSTEMS", comments = "JCR(2015): 2.476 Position: 17/143 (Q1) Category: COMPUTER SCIENCE, INFORMATION SYSTEMS", doi = "10.1109/TKDE.2014.2365780", issn = "1041-4347", journal = "IEEE Transactions on Knowledge and Data Engineering", keywords = "Over-sampling, imbalanced classification, ordinal regression, ordinal classification", month = "May", note = "JCR(2015): 2.476 Position: 17/143 (Q1) Category: COMPUTER SCIENCE, INFORMATION SYSTEMS", number = "5", pages = "1233--1245", title = "{G}raph-{B}ased {A}pproaches for {O}ver-sampling in the context of {O}rdinal {R}egression", url = "http://www.uco.es/grupos/ayrna/GBOforOR", volume = "27", year = "2015", }

Note:

JCR(2015): 2.476 Position: 17/143 (Q1) Category: COMPUTER SCIENCE, INFORMATION SYSTEMS

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.

Comments:

JCR(2015): 2.476 Position: 17/143 (Q1) Category: COMPUTER SCIENCE, INFORMATION SYSTEMS