Bayes Optimal Multilabel Classification via Probabilistic Classifier Chains
Abstract
In the realm of multilabel classification (MLC), it has become an opinio communis that optimal predictive performance can only be achieved by learners that explicitly take label dependence into account. The goal of this paper is to elaborate on this postulate in a critical way. To this end, we formalize and analyze MLC within a probabilistic setting. Thus, it becomes possible to look at the problem from the point of view of risk minimization and Bayes optimal prediction. Moreover, inspired by our probabilistic setting, we propose a new method for MLC that generalizes and outperforms another approach, called classifier chains, that was recently introduced in the literature.
Cite
Text
Dembczynski et al. "Bayes Optimal Multilabel Classification via Probabilistic Classifier Chains." International Conference on Machine Learning, 2010.Markdown
[Dembczynski et al. "Bayes Optimal Multilabel Classification via Probabilistic Classifier Chains." International Conference on Machine Learning, 2010.](https://mlanthology.org/icml/2010/dembczynski2010icml-bayes/)BibTeX
@inproceedings{dembczynski2010icml-bayes,
title = {{Bayes Optimal Multilabel Classification via Probabilistic Classifier Chains}},
author = {Dembczynski, Krzysztof and Cheng, Weiwei and Hüllermeier, Eyke},
booktitle = {International Conference on Machine Learning},
year = {2010},
pages = {279-286},
url = {https://mlanthology.org/icml/2010/dembczynski2010icml-bayes/}
}