An interpretable dynamic ensemble selection multiclass imbalance approach with ensemble imbalance learning for predicting road crash injury severity
Abstract Accurate prediction of crash injury severity and understanding the seriousness of multi-classification injuries is vital for informing authorities and the public. This Knowledge is crucial for enhancing road safety and reducing congestion, as different levels of injury necessitate distinct...
Saved in:
| Main Authors: | Kamran Aziz, Feng Chen, Mahmood Ahmad, Muhammad Salman Khan, Mohanad Muayad Sabri Sabri, Hamad Almujibah |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-08935-x |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Advanced Fraud Detection: Leveraging K-SMOTEENN and Stacking Ensemble to Tackle Data Imbalance and Extract Insights
by: Nurafni Damanik, et al.
Published: (2025-01-01) -
Ensemble Learning with Highly Variable Class-Based Performance
by: Brandon Warner, et al.
Published: (2024-09-01) -
Comprehensive Performance Assessment of Multi-Neural Ensemble Model for Mortality Prediction in ICU
by: M. Fathima Begum, et al.
Published: (2025-01-01) -
The Impact of the SMOTE Method on Machine Learning and Ensemble Learning Performance Results in Addressing Class Imbalance in Data Used for Predicting Total Testosterone Deficiency in Type 2 Diabetes Patients
by: Mehmet Kivrak, et al.
Published: (2024-11-01) -
SMOTEHashBoost: Ensemble Algorithm for Imbalanced Dataset Pattern Classification
by: Seema Yadav, et al.
Published: (2025-01-01)