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Entropy-based model-free feature screening for ultrahigh-dimensional multiclass classification

机译:基于熵的无模型特征筛选用于超高维多类分类

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摘要

Most feature screening methods for ultrahigh-dimensional classification explicitly or implicitly assume the covariates are continuous. However, in the practice, it is quite common that both categorical and continuous covariates appear in the data, and applicable feature screening method is very limited. To handle this non-trivial situation, we propose an entropy-based feature screening method, which is model free and provides a unified screening procedure for both categorical and continuous covariates. We establish the sure screening and ranking consistency properties of the proposed procedure. We investigate the finite sample performance of the proposed procedure by simulation studies and illustrate the method by a real data analysis.
机译:用于超高维分类的大多数特征筛选方法显式或隐式假定协变量是连续的。但是,在实践中,分类协变量和连续协变量都出现在数据中非常普遍,并且适用的特征筛选方法非常有限。为了处理这种非平凡的情况,我们提出了一种基于熵的特征筛选方法,该方法免费提供模型,并为分类协变量和连续协变量提供了统一的筛选程序。我们建立了建议程序的确定筛选和​​排序一致性属性。我们通过仿真研究来研究所提出程序的有限样本性能,并通过实际数据分析来说明该方法。

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