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Binary ranking for ordinal class imbalance

  • Ricardo Cruz
  • Kelwin Fernandes
  • Joaquim F. Pinto Costa
  • María Pérez Ortiz
  • Jaime S. Cardoso
Original Article
  • 72 Downloads

Abstract

Imbalanced classification has been extensively researched in the last years due to its prevalence in real-world datasets, ranging from very different topics such as health care or fraud detection. This literature has long been dominated by variations of the same family of solutions (e.g. mainly resampling and cost-sensitive learning). Recently, a new and promising way of tackling this problem has been introduced: learning with scoring pairwise ranking so that each pair of classes contribute in tandem to the decision boundary. In this sense, the paper addresses the problem of class imbalance in the context of ordinal regression, proposing two novel contributions: (a) approaching the imbalance by binary pairwise ranking using a well-known label decomposition ensemble, and (b) introducing a regularization into this ensemble so that parallel decision boundaries are favored. These are two independent contributions that synergize well. Our model is tested using linear Support Vector Machines and our results are compared against state-of-the-art models. Both approaches show promising performance in ordinal class imbalance, with an overall 15% improvement relative to the state-of-the-art, as evaluated by a balanced metric.

Keywords

Ordinal classification Ordinal regression Class imbalance Learning to rank Ranking 

Notes

Acknowledgements

This work was funded by the Project “NanoSTIMA: Macro-to-Nano Human Sensing: Towards Integrated Multimodal Health Monitoring and Analytics/NORTE-01-0145-FEDER-000016” financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF), and also by Fundação para a Ciência e a Tecnologia (FCT) within PhD grant numbers SFRH/BD/122248/2016 and SFRH/BD/93012/2013.

References

  1. 1.
    Cardoso JS, Costa JF (2007) Learning to classify ordinal data: the data replication method. J Mach Learn Res 8((Jul)):1393–1429MathSciNetzbMATHGoogle Scholar
  2. 2.
    Chu W, Keerthi SS (2007) Support vector ordinal regression. Neural Comput 19(3):792–815MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Costa JFP, Sousa R, Cardoso JS (2010) An all-at-once unimodal svm approach for ordinal classification. In: 2010 Ninth international conference on machine learning and applications (ICMLA), IEEE, pp 59–64Google Scholar
  4. 4.
    Cruz R, Fernandes K, Cardoso JS, Costa JFP (2016) Tackling class imbalance with ranking. In: 2016 International joint conference on neural networks (IJCNN), IEEE, pp 2182–2187Google Scholar
  5. 5.
    Cruz R, Fernandes K, Costa JFP, Ortiz MP, Cardoso JS (2017) Combining ranking with traditional methods for ordinal class imbalance. In: International work-conference on artificial neural networks. Springer, Cham, pp 538–548Google Scholar
  6. 6.
    Cruz R, Fernandes K, Costa JFP, Ortiz MP, Cardoso JS (2017) Ordinal class imbalance with ranking. In: Iberian conference on pattern recognition and image analysis. Springer, Cham, pp 3–12Google Scholar
  7. 7.
    Denil M, Trappenberg TP (2010) Overlap versus imbalance. In: Canadian conference on AI. Springer, pp 220–231Google Scholar
  8. 8.
    Frank E, Hall M (2001) A simple approach to ordinal classification. In: Machine learning: ECML 2001, pp 145–156Google Scholar
  9. 9.
    Gutiérrez PA, Pérez-Ortiz M, Sanchez-Monedero J, Fernández-Navarro F, Hervas-Martinez C (2016) Ordinal regression methods: survey and experimental study. IEEE Trans Knowl Data Eng 28(1):127–146CrossRefGoogle Scholar
  10. 10.
    Herbrich R, Graepel T, Obermayer K (1999) Support vector learning for ordinal regression. In: Ninth international conference on artificial neural networks ICANN 99, vol 1, Edinburgh, pp 97–102Google Scholar
  11. 11.
    Li L, Lin HT (2007) Ordinal regression by extended binary classification. In: Advances in neural information processing systems, pp 865–872Google Scholar
  12. 12.
    Pérez-Ortiz M, Gutiérrez PA, Hervás-Martínez C, Yao X (2015) Graph-based approaches for over-sampling in the context of ordinal regression. IEEE Trans Knowl Data Eng 27(5):1233–1245CrossRefGoogle Scholar
  13. 13.
    Shalev-Shwartz S, Singer Y, Srebro N, Cotter A (2011) Pegasos: primal estimated sub-gradient solver for svm. Math Program 127(1):3–30MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Wang H, Shi Y, Niu L, Tian Y (2017) Nonparallel support vector ordinal regression. IEEE Trans Cybern 47(10):3306–3317CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.INESC TECPortoPortugal
  2. 2.Faculty of EngineeringUniversity of PortoPortoPortugal
  3. 3.Mathematics Department and CMUP, Faculty of SciencesUniversity of PortoPortoPortugal
  4. 4.Computer LaboratoryUniversity of CambridgeCambridgeUK

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