Mislabeled examples detection viewed as probing machine learning models: concepts, survey and extensive benchmark
Abstract
Mislabeled examples are ubiquitous in real-world machine learning datasets, advocating the development of techniques for automatic detection. We show that most mislabeled detection methods can be viewed as probing trained machine learning models using a few core principles. We formalize a modular framework that encompasses these methods, parameterized by only 4 building blocks, as well as a Python library that demonstrates that these principles can actually be implemented. The focus is on classifier-agnostic concepts, with an emphasis on adapting methods developed for deep learning models to non-deep classifiers for tabular data. We benchmark existing methods on (artificial) Completely At Random (NCAR) as well as (realistic) Not At Random (NNAR) labeling noise from a variety of tasks with imperfect labeling rules. This benchmark provides new insights as well as limitations of existing methods in this setup.
Cite
@article{arxiv.2410.15772,
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= {arXiv preprint arXiv:2410.15772},
year = {2024}
}