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Updated: Feb 20, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

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改进的平衡随机森林 (iBRF):在不平衡的数据集中进行碰撞严重程度分类的灵活混合重新采样-背包框架

Seyed Iman Mohammadpour1, Javadreza Vahedi1

  • 1Department of Civil Engineering, Faculty of Engineering, University of Bojnord, Bojnord, Iran.

Traffic injury prevention
|February 18, 2026
PubMed
概括

在事故数据中的阶级不平衡是一个挑战. 改进平衡随机森林 (iBRF) 模型有效地解决了这一问题,通过结合重新采样和组合方法,改善严重撞击结果分类.

关键词:
在CTGANs中.斯莫特 (SMOTE) 是一种类型.阶级不平衡,就是阶级不平衡.撞车严重程度建模的撞车严重程度建模组合学习组合学习组合学习根据iBRF的说法,这是一个很好的方法.

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