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

Published: 17 Oct 2024, Last Modified: 31 Oct 2024Accepted by TMLREveryoneRevisionsBibTeXCC BY 4.0
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
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