Risky Driver Recognition with Class Imbalance Data and Automated Machine Learning Framework

Identifying high-risk drivers before an accident happens is necessary for traffic accident control and prevention. Due to the class-imbalance nature of driving data, high-risk samples as the minority class are usually ill-treated by standard classification algorithms. Instead of applying preset samp...

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Detalles Bibliográficos
Autores principales: Wang, Ke, Xue, Qingwen, Lu, Jian John
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8305749/
https://www.ncbi.nlm.nih.gov/pubmed/34299986
http://dx.doi.org/10.3390/ijerph18147534

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8305749/
https://www.ncbi.nlm.nih.gov/pubmed/34299986
http://dx.doi.org/10.3390/ijerph18147534

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