EN. Detect mislabeled examples in machine learning dataset, using the 4 components framework described in the paper Mislabeled examples detection viewed as probing machine learning models: concepts, survey and extensive benchmark, which allows the implementation of a variety of model-probing detection methods.
FR. Détection d'exemples mal-étiquetés dans des jeux de données d'apprentissage automatique, en utilisant les 4 composants décrits dans l'article Mislabeled examples detection viewed as probing machine learning models: concepts, survey and extensive benchmark, qui permet d'implémenter une multitude de méthodes de détection par sondage de modèle.
If you use this library in a research project, please consider citing the corresponding paper with the following bibtex entry:
@article{george2024mislabeled,
title={Mislabeled examples detection viewed as probing machine learning models: concepts, survey and extensive benchmark},
author={Thomas George and Pierre Nodet and Alexis Bondu and Vincent Lemaire},
journal={Transactions on Machine Learning Research},
issn={2835-8856},
year={2024},
url={https://openreview.net/forum?id=3YlOr7BHkx},
note={}
}