Crash Injury Severity Prediction Using an Ordinal Classification Machine Learning Approach
In many related works, nominal classification algorithms ignore the order between injury severity levels and make sub-optimal predictions. Existing ordinal classification methods suffer rank inconsistency and rank non-monotonicity. The aim of this paper is to propose an ordinal classification approa...
Saved in:
Main Authors: | Shengxue Zhu, Ke Wang, Chongyi Li |
---|---|
Format: | article |
Language: | EN |
Published: |
MDPI AG
2021
|
Subjects: | |
Online Access: | https://doaj.org/article/3b6e2cb0b88b4ac1a903d470968771d1 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Assessment on the crash risk factors of a typical long-span bridge using oversampling-based classification method and considering bridge structure movement
by: Peiyan Chen, et al.
Published: (2021) -
What factors results in having a severe crash? a closer look on distraction-related factors
by: Hesamoddin Razi-Ardakani, et al.
Published: (2019) -
Injury prediction for advanced automatic crash notification system
by: Ying LU, et al.
Published: (2021) -
Predicting Out-of-Stock Using Machine Learning: An Application in a Retail Packaged Foods Manufacturing Company
by: Juan Manuel Rozas Andaur, et al.
Published: (2021) -
Comparison of Ordinal Response Modeling Methods like Decision Trees, Ordinal Forest and L1 Penalized Continuation Ratio Regression in High Dimensional Data
by: Zahra Torkashvand, et al.
Published: (2021)