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Updated: Jul 5, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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FHBF:在高度不平衡的临床数据集中,联邦混合增强森林具有监督学习任务的脱学率

Vasileios C Pezoulas1, Fanis Kalatzis1, Themis P Exarchos1,2

  • 1Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, 45110 Ioannina, Greece.

Patterns (New York, N.Y.)
|January 24, 2024
PubMed
概括

联合混合增强森林 (FHBF) 有效地打击过度合在不平衡数据的联合学习. 这种新的算法提高了用于淋巴瘤发育检测的AI模型的稳定性.

关键词:
阶级不平衡 阶级不平衡放弃学业的人 放弃学业的人联合学习的联合学习梯度增强可以提高梯度.混合损失函数的混合损失函数机器学习是机器学习.

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