Impact of Label Noise on the Learning Based Models for a Binary Classification of Physiological Signal

Label noise is omnipresent in the annotations process and has an impact on supervised learning algorithms. This work focuses on the impact of label noise on the performance of learning models by examining the effect of random and class-dependent label noise on a binary classification task: quality a...

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Detalles Bibliográficos
Autores principales: Ding, Cheng, Pereira, Tania, Xiao, Ran, Lee, Randall J., Hu, Xiao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9572105/
https://www.ncbi.nlm.nih.gov/pubmed/36236265
http://dx.doi.org/10.3390/s22197166

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9572105/
https://www.ncbi.nlm.nih.gov/pubmed/36236265
http://dx.doi.org/10.3390/s22197166

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