MORD: Multi-Class Classifier for Ordinal Regression
Abstract
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.
Cite
Text
Antoniuk et al. "MORD: Multi-Class Classifier for Ordinal Regression." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2013. doi:10.1007/978-3-642-40994-3_7Markdown
[Antoniuk et al. "MORD: Multi-Class Classifier for Ordinal Regression." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2013.](https://mlanthology.org/ecmlpkdd/2013/antoniuk2013ecmlpkdd-mord/) doi:10.1007/978-3-642-40994-3_7BibTeX
@inproceedings{antoniuk2013ecmlpkdd-mord,
title = {{MORD: Multi-Class Classifier for Ordinal Regression}},
author = {Antoniuk, Kostiantyn and Franc, Vojtech and Hlavác, Václav},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2013},
pages = {96-111},
doi = {10.1007/978-3-642-40994-3_7},
url = {https://mlanthology.org/ecmlpkdd/2013/antoniuk2013ecmlpkdd-mord/}
}