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

TL;DR

This paper presents a unified framework and extensive benchmark for detecting mislabeled examples in machine learning datasets, emphasizing model probing techniques applicable across different classifiers and data types.

Contribution

It introduces a modular, formalized framework for mislabeled detection methods, along with a Python library and comprehensive benchmarking across various noise scenarios.

Findings

Existing methods have limitations in real-world noise conditions

The benchmark reveals strengths and weaknesses of current detection techniques

Framework facilitates adaptation to different classifiers and data types

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.