Classification in the Presence of Label Noise

Frenay, B., & Verleysen, M. (2014). Classification in the Presence of Label Noise: A Survey. IEEE Transactions on Neural Networks and Learning Systems, 25(5), 845-869.

Type

Journal article

Intellectual base

Primary research

Year

2014

Volume

25

Issue

5

Pages

845-869

DOI

10.1109/tnnls.2013.2292894

DOI registrant

Institute of Electrical and Electronics Engineers

Link

https://doi.org/10.1109/TNNLS.2013.2292894

Cited from

philosophy

Cited by

2 citations across 2 artifacts.

Each citation links to the sentence it supports in the citing article.

Primes

• Ground Truth
  • Machine learning and statistics: labels in supervised learning are the canonical ground truth, and the literature on label noise, inter-annotator agreement, and reference-set construction is exactly the literature of ground-truth-as-construct.

    This sourceSurveys label noise in supervised learning and how reference-label error bounds achievable model accuracy.

Domain-specific

• Label noise
  • Label noise changes empirical risk and can drive memorization, miscalibration, subgroup error, and biased evaluation; instance-independent, class-conditional, and instance-dependent noise require different identification assumptions.

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