We show that classification rules used in ordinal regression are equivalent to a certain class of linear multi-class classifiers. This observation not only allows to design new learning algorithms for ordinal regression using existing methods for multi-class classification but it also allows to derive new models for ordinal regression. For example, one can convert learning of ordinal classifier with (almost) arbitrary loss function to a convex unconstrained risk minimization problem for which many efficient solvers exist. The established equivalence also allows to increase discriminative power of the ordinal classifier without need to use kernels by introducing a piece-wise ordinal classifier. We demonstrate advantages of the proposed models on standard benchmarks as well as in solving a real-life problem. In particular, we show that the proposed piece-wise ordinal classifier applied to visual age estimation outperforms other standard prediction models. © 2013 Springer-Verlag.
CITATION STYLE
Antoniuk, K., Franc, V., & Hlaváč, V. (2013). MORD: Multi-class classifier for ordinal regression. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8190 LNAI, pp. 96–111). https://doi.org/10.1007/978-3-642-40994-3_7
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