Handling Imbalanced Data in Road Crash Severity Prediction by Machine Learning Algorithms

Crash severity is undoubtedly a fundamental aspect of a crash event. Although machine learning algorithms for predicting crash severity have recently gained interest by the academic community, there is a significant trend towards neglecting the fact that crash datasets are acutely imbalanced. Overlo...

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Veröffentlicht in:	Infrastructures (Basel) Jg. 5; H. 7; S. 61
Hauptverfasser:	Fiorentini, Nicholas, Losa, Massimo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 20.07.2020
Schlagworte:	Algorithms Classification Classifiers crash severity Crashes Datasets Decision making Decision trees k-nearest neighbor Machine learning machine learning classification algorithms Neural networks Performance evaluation Prediction models Property damage random classification tree random forest random undersampling the majority class Regression Roads & highways Studies
ISSN:	2412-3811, 2412-3811
Online-Zugang:	Volltext
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