| Autor: |
George, Thomas, Nodet, Pierre, Bondu, Alexis, Lemaire, Vincent |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Transactions on Machine Learning Research 2024 |
| Druh dokumentu: |
Working Paper |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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