A Survey of Methods for Detection and Correction of Noisy Labels in Time Series Data - Artificial Intelligence Applications and Innovations
Conference Papers Year : 2021

A Survey of Methods for Detection and Correction of Noisy Labels in Time Series Data

Gentry Atkinson
  • Function : Author
  • PersonId : 1105380
Vangelis Metsis
  • Function : Author
  • PersonId : 1105381

Abstract

Mislabeled data in large datasets can quickly degrade the performance of machine learning models. There is a substantial base of work on how to identify and correct instances in data with incorrect annotations. However, time series data pose unique challenges that often are not accounted for in label noise detecting platforms. This paper reviews the body of literature concerning label noise and methods of dealing with it, with a focus on applicability to time series data. Time series data visualization and feature extraction techniques used in the denoising process are also discussed.
Fichier principal
Vignette du fichier
509922_1_En_38_Chapter.pdf (528.06 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-03287653 , version 1 (15-07-2021)

Licence

Identifiers

Cite

Gentry Atkinson, Vangelis Metsis. A Survey of Methods for Detection and Correction of Noisy Labels in Time Series Data. 17th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2021, Hersonissos, Crete, Greece. pp.479-493, ⟨10.1007/978-3-030-79150-6_38⟩. ⟨hal-03287653⟩
113 View
104 Download

Altmetric

Share

More