A Reduction of Label Ranking to Multiclass Classification
Abstract
Label ranking considers the problem of learning a mapping from instances to strict total orders over a predefined set of labels. In this paper, we present a framework for label ranking using a decomposition into a set of multiclass problems . Conceptually, our approach can be seen as a generalization of pairwise preference learning. In contrast to the latter, it allows for controlling the granularity of the decomposition, varying between binary preferences and complete rankings as extreme cases. It is specifically motivated by limitations of pairwise learning with regard to the minimization of certain loss functions. We discuss theoretical properties of the proposed method in terms of accuracy, error correction, and computational complexity. Experimental results are promising and indicate that improvements upon the special case of pairwise preference decomposition are indeed possible.
Cite
Text
Brinker and Hüllermeier. "A Reduction of Label Ranking to Multiclass Classification." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2019. doi:10.1007/978-3-030-46133-1_13Markdown
[Brinker and Hüllermeier. "A Reduction of Label Ranking to Multiclass Classification." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2019.](https://mlanthology.org/ecmlpkdd/2019/brinker2019ecmlpkdd-reduction/) doi:10.1007/978-3-030-46133-1_13BibTeX
@inproceedings{brinker2019ecmlpkdd-reduction,
title = {{A Reduction of Label Ranking to Multiclass Classification}},
author = {Brinker, Klaus and Hüllermeier, Eyke},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2019},
pages = {204-219},
doi = {10.1007/978-3-030-46133-1_13},
url = {https://mlanthology.org/ecmlpkdd/2019/brinker2019ecmlpkdd-reduction/}
}