Mislabeled examples detection viewed as probing machine learning models: concepts, survey and extensive benchmark

Thomas George, Pierre Nodet, Alexis Bondu, Vincent Lemaire

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.

Submission Length: Long submission (more than 12 pages of main content)

Video: https://youtu.be/fT9VZXs0nh8

Code: https://github.com/Orange-OpenSource/mislabeled

Assigned Action Editor: ~Aditya_Menon1

Submission Number: 2960