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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Multi-class Classification with Reject Option and Performance Guarantees using Conformal Prediction |
Proceedings of the Thirteenth Symposium on Conformal and Probabilistic Prediction with Applications |
2024 |
230 |
Proceedings of Machine Learning Research |
0 |
PMLR |
Beyond the standard classification scenario, allowing a classifier to refrain from making a prediction under uncertainty can have advantages in safety-critical applications, where a mistake may hold great costs. In this paper, we extend previous works on the development of classifiers with reject option grounded on the conformal prediction framework. Specifically, our work introduces a novel approach for inducing multi-class classifiers with reliable accuracy or recall estimates for a given rejection rate. We empirically evaluate our suggested approach in six multi-class datasets and demonstrate its effectiveness against both calibrated and uncalibrated probabilistic classifiers. The results underscore our method’s capability to provide reliable error rate estimates, thereby enhancing decision-making processes where erroneous predictions bear critical consequences. |
inproceedings |
2640-3498 |
garcia-galindo24a |
Multi-class Classification with Reject Option and Performance Guarantees using Conformal Prediction |
295 |
314 |
295-314 |
295 |
false |
Vantini, Simone and Fontana, Matteo and Solari, Aldo and Bostr\"{o}m, Henrik and Carlsson, Lars |
|
Garc\'ia-Galindo, Alberto and L\'opez-De-Castro, Marcos and Arma\~nanzas, Rub\'en |
|
2024-09-10 |
Proceedings of the Thirteenth Symposium on Conformal and Probabilistic Prediction with Applications |
inproceedings |
|