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‘If Only I Would Have Done that…’: A Controlled Adaptive Network Model for Learning by Counterfactual Thinking Kapitel lesen Erstes Kapitel lesen

2021 | OriginalPaper | Buchkapitel

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

verfasst von : Gentry Atkinson, Vangelis Metsis

Erschienen in: Artificial Intelligence Applications and Innovations

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel BEMS in the Era of Internet of Energy: A Review
Nächstes Kapitel An Automated Tool to Support an Intelligence Learner Management System Using Learning Analytics and Machine Learning
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Metadaten
Titel
A Survey of Methods for Detection and Correction of Noisy Labels in Time Series Data
verfasst von
Gentry Atkinson
Vangelis Metsis
Copyright-Jahr
2021
Buch
Artificial Intelligence Applications and Innovations

Print ISBN: 978-3-030-79149-0

Electronic ISBN: 978-3-030-79150-6

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-3-030-79150-6_38

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