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A machine learning based variable selection algorithm for binary classification of perinatal mortality
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DOI：10.1371/journal.pone.0315498.png)
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摘要
En 中文
在分类领域中，识别具有更高模型性能的显著预测因子是关键目标。一种基于机器学习的变量选择技术，即通过结合竞争性自适应重加权采样（CARS）和逻辑回归，提出了用于二元分类的CARS-逻辑回归模型。基于五项评估标准，发现所提出的方法比前向选择逻辑回归模型更高效。CARS-逻辑回归模型被用于确定巴基斯坦围产期死亡率中的显著因素。所识别的危险因素揭示了社会、文化、经济和健康相关的特征，这些特征包含了关于巴基斯坦围产期死亡率的关键信息，供决策者参考。
Keyword:
REWEIGHTED SAMPLING METHOD
WAVELENGTHS
期刊
IF:
2.6
论文数:
2.6W
被引数:
81.6W
机构
引用论文
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