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February 21, 2026Traffic Injury Prevention2 citations

Improved Balanced Random Forest (iBRF): A flexible hybrid resampling-bagging framework for crash severity classification in imbalanced datasets

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SMSeyed Iman MohammadpourJVJavadreza Vahedi

Key Points

  • The research aims to enhance crash severity classification using a hybrid iBRF algorithm, focusing on imbalanced datasets.
  • Developed the Improved Balanced Random Forest algorithm
  • Implemented a hybrid resampling and bagging approach
  • Analyzed performance with imbalanced datasets
  • Enhanced classification performance for crash severity
  • Identified key risk factors for severe-outcome crashes
  • Showed improved detection of minority class instances

Abstract

The iBRF algorithm not only enhances classification performance but also helps identify risk factors associated with the minority class, which almost always correspond to severe‑outcome crashes and the focus of studies.

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Cite This Study

Mohammadpour et al. (2026) studied this question.

synapsesocial.com/papers/69994bdd873532290d01fde6https://doi.org/10.1080/15389588.2025.2610428
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