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Erschienen in:
Weighted Dissemination of Bundles in Probabilistic Spray and Wait Routing Protocol Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

Impact of Noisy Labels in Learning Techniques: A Survey

verfasst von : Nitika Nigam, Tanima Dutta, Hari Prabhat Gupta

Erschienen in: Advances in Data and Information Sciences

Verlag: Springer Singapore

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Abstract

Noisy data is the main issue in classification. The possible sources of noise label can be insufficient availability of information or encoding/communication problems, or data entry error by experts/nonexperts, etc., which can deteriorate the model’s performance and accuracy. However, in a real-world dataset, like Flickr, the likelihood of containing the noisy label is high. Initially, few methods such as identification, correcting, and elimination of noisy data was used to enhance the performance. Various machine learning algorithms are used to diminish the noisy environment, but in the recent studies, deep learning models are resolving this issue. In this survey, a brief introduction about the solution for the noisy label is provided.

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Vorheriges Kapitel Classification of Forest Cover Type Using Random Forests Algorithm
Nächstes Kapitel Performance Analysis of Schema Design Approaches for Migration from RDBMS to NoSQL Databases
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Metadaten
Titel
Impact of Noisy Labels in Learning Techniques: A Survey
verfasst von
Nitika Nigam
Tanima Dutta
Hari Prabhat Gupta
Copyright-Jahr
2020
Verlag
Springer Singapore
Buch
Advances in Data and Information Sciences

Print ISBN: 978-981-15-0693-2

Electronic ISBN: 978-981-15-0694-9

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-981-15-0694-9_38

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