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Maximizing upgrading and downgrading margins for ordinal regression

Author

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  • Emilio Carrizosa
  • Belen Martin-Barragan

Abstract

In ordinal regression, a score function and threshold values are sought to classify a set of objects into a set of ranked classes. Classifying an individual in a class with higher (respectively lower) rank than its actual rank is called an upgrading (respectively downgrading) error. Since upgrading and downgrading errors may not have the same importance, they should be considered as two different criteria to be taken into account when measuring the quality of a classifier. In Support Vector Machines, margin maximization is used as an effective and computationally tractable surrogate of the minimization of misclassification errors. As an extension, we consider in this paper the maximization of upgrading and downgrading margins as a surrogate of the minimization of upgrading and downgrading errors, and we address the biobjective problem of finding a classifier maximizing simultaneously the two margins. The whole set of Pareto-optimal solutions of such biobjective problem is described as translations of the optimal solutions of a scalar optimization problem. For the most popular case in which the Euclidean norm is considered, the scalar problem has a unique solution, yielding that all the Pareto-optimal solutions of the biobjective problem are translations of each other. Hence, the Pareto-optimal solutions can easily be provided to the analyst, who, after inspection of the misclassification errors caused, should choose in a later stage the most convenient classifier. The consequence of this analysis is that it provides a theoretical foundation for a popular strategy among practitioners, based on the so-called ROC curve, which is shown here to equal the set of Pareto-optimal solutions of maximizing simultaneously the downgrading and upgrading margins. Copyright Springer-Verlag 2011

Suggested Citation

  • Emilio Carrizosa & Belen Martin-Barragan, 2011. "Maximizing upgrading and downgrading margins for ordinal regression," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 74(3), pages 381-407, December.
    • Handle: RePEc:spr:mathme:v:74:y:2011:i:3:p:381-407
    DOI: 10.1007/s00186-011-0368-z
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    References listed on IDEAS

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    1. Carrizosa, Emilio & Martin-Barragan, Belen, 2006. "Two-group classification via a biobjective margin maximization model," European Journal of Operational Research, Elsevier, vol. 173(3), pages 746-761, September.
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    3. Nakayama, Hirotaka & Yun, Ye Boon & Asada, Takeshi & Yoon, Min, 2005. "MOP/GP models for machine learning," European Journal of Operational Research, Elsevier, vol. 166(3), pages 756-768, November.
    4. Tianshi Jiao & Jiming Peng & Tamás Terlaky, 2009. "A confidence voting process for ranking problems based on support vector machines," Annals of Operations Research, Springer, vol. 166(1), pages 23-38, February.
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