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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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改善されたバランスされたランダムフォレスト (iBRF):不均衡のデータセットにおけるクラッシュの重度の分類のための柔軟なハイブリッド再サンプリングバックの枠組み

Seyed Iman Mohammadpour1, Javadreza Vahedi1

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

Traffic injury prevention
|February 18, 2026
PubMed
まとめ

クラッシュデータにおけるクラス不均衡は課題です. 改善されたバランスされたランダムフォレスト (iBRF) モデルは,再サンプリングとアンサンブル方法を組み合わせて,重度の衝突結果の分類を改善することで,これを効果的に対処します.

キーワード:
CTGANsは,CTGANsと一致している.スモート (SMOTE) とは階級の不均衡は,階級の不均衡である.クラッシュ重度のモデリングアンサンブル・ラーニングとはアンサンブル・ラーニングのことです.iBRFFは,iBRFを構成している.

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