基于条件信息熵的超高维分类数据特征筛选
发布时间：2019-02-14 19:02
【摘要】：文章提出了一种基于条件信息熵的超高维自由模型下非参数特征筛选方法,在响应变量为两类别时,对多类别离散型协变量进行特征筛选。通过理论证明和蒙特卡罗数值模拟验证了该筛选方法具有确定筛选性质,对超高维分类变量的重要特征筛选具有显著的效果。
[Abstract]:In this paper, a non-parametric feature selection method based on conditional information entropy for ultra-high dimensional free model is proposed. When the response variable is two classes, the multi-class discrete covariable is selected. The theoretical proof and Monte Carlo numerical simulation show that the method has definite screening properties and has remarkable effect on the important feature screening of ultra-high dimensional classification variables.
【作者单位】： 南京信息工程大学数学与统计学院
【基金】：国家自然科学基金资助项目(11301279) 江苏省自然科学基金资助项目(BK20140983;BK20161530) 江苏省“青蓝工程”资助项目(2016)
【分类号】：O21;O236
本文编号：2422501
[Abstract]:In this paper, a non-parametric feature selection method based on conditional information entropy for ultra-high dimensional free model is proposed. When the response variable is two classes, the multi-class discrete covariable is selected. The theoretical proof and Monte Carlo numerical simulation show that the method has definite screening properties and has remarkable effect on the important feature screening of ultra-high dimensional classification variables.
【作者单位】： 南京信息工程大学数学与统计学院
【基金】：国家自然科学基金资助项目(11301279) 江苏省自然科学基金资助项目(BK20140983;BK20161530) 江苏省“青蓝工程”资助项目(2016)
【分类号】：O21;O236
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相关硕士学位论文 前1条
1 孙超男;基于条件信息熵的超高维分类数据特征筛选[D];南京信息工程大学;2017年
，本文编号：2422501
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