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

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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Bibliographic Details
Main Authors: Ke Wang, Qingwen Xue, Jian John Lu
Format: Article
Language:English
Published: MDPI AG 2021-07-01
Series:International Journal of Environmental Research and Public Health
Subjects:
risky driving
automated machine learning
imbalanced data
sampling
cost-sensitive learning
probability calibration
Online Access:https://www.mdpi.com/1660-4601/18/14/7534

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https://www.mdpi.com/1660-4601/18/14/7534

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