title | booktitle | year | volume | series | month | publisher | url | abstract | layout | issn | id | tex_title | firstpage | lastpage | page | order | cycles | bibtex_editor | editor | bibtex_author | author | date | address | container-title | genre | issued | extras | |||||||||||||||||||||||||||||||||||||||||||||||||||
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Distribution-free Conformal Prediction for Ordinal Classification |
Proceedings of the Thirteenth Symposium on Conformal and Probabilistic Prediction with Applications |
2024 |
230 |
Proceedings of Machine Learning Research |
0 |
PMLR |
Multi-label classification is a common challenge in various machine learning applications, where a single data instance can be associated with multiple classes simultaneously. The current paper proposes a novel tree-based method for multi-label classification using conformal prediction and multiple hypothesis testing. The proposed method employs hierarchical clustering with labelsets to develop a hierarchical tree, which is then formulated as a multiple-testing problem with a hierarchical structure. The split-conformal prediction method is used to obtain marginal conformal |
inproceedings |
2640-3498 |
chakraborty24a |
Distribution-free Conformal Prediction for Ordinal Classification |
120 |
139 |
120-139 |
120 |
false |
Vantini, Simone and Fontana, Matteo and Solari, Aldo and Bostr\"{o}m, Henrik and Carlsson, Lars |
|
Chakraborty, Subhrasish and Tyagi, Chhavi and Qiao, Haiyan and Guo, Wenge |
|
2024-09-10 |
Proceedings of the Thirteenth Symposium on Conformal and Probabilistic Prediction with Applications |
inproceedings |
|