Multilabel Classification with Label Correlations and Missing Labels
Abstract
Many real-world applications involve multilabel classification, in which the labels can have strong inter-dependencies and some of them may even be missing.Existing multilabel algorithms are unable to handle both issues simultaneously.In this paper, we propose a probabilistic model that can automatically learn and exploit multilabel correlations.By integrating out the missing information, it also provides a disciplinedapproach to the handling of missing labels. The inference procedure is simple, and the optimization subproblems are convex. Experiments on a number of real-world data sets with both complete and missing labelsdemonstrate that the proposed algorithm can consistently outperform state-of-the-art multilabel classification algorithms.
Cite
Text
Bi and Kwok. "Multilabel Classification with Label Correlations and Missing Labels." AAAI Conference on Artificial Intelligence, 2014. doi:10.1609/AAAI.V28I1.8996Markdown
[Bi and Kwok. "Multilabel Classification with Label Correlations and Missing Labels." AAAI Conference on Artificial Intelligence, 2014.](https://mlanthology.org/aaai/2014/bi2014aaai-multilabel/) doi:10.1609/AAAI.V28I1.8996BibTeX
@inproceedings{bi2014aaai-multilabel,
title = {{Multilabel Classification with Label Correlations and Missing Labels}},
author = {Bi, Wei and Kwok, James T.},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2014},
pages = {1680-1686},
doi = {10.1609/AAAI.V28I1.8996},
url = {https://mlanthology.org/aaai/2014/bi2014aaai-multilabel/}
}