论文标题
在超高维二元分类中进行精确的特征筛选
On Exact Feature Screening in Ultrahigh-dimensional Binary Classification
论文作者
论文摘要
我们提出了一种基于超高维二进制分类问题的能量距离的新的无模型功能筛选方法。提出的方法很高，在丢弃所有噪声变量后仅保留相关特征。提出的筛选方法还扩展了以识别一对略有未检测但其联合分布差异的变量。最后，我们建立了一个分类器，该分类器在提出的特征选择标准和歧视方法之间保持连贯性，并确立其风险一致性。对模拟和真实基准数据集的一项广泛的数值研究表明，我们所提出的方法比最先进的方法具有清晰而令人信服的优势。
We propose a new model-free feature screening method based on energy distances for ultrahigh-dimensional binary classification problems. With a high probability, the proposed method retains only relevant features after discarding all the noise variables. The proposed screening method is also extended to identify pairs of variables that are marginally undetectable but have differences in their joint distributions. Finally, we build a classifier that maintains coherence between the proposed feature selection criteria and discrimination method and also establish its risk consistency. An extensive numerical study with simulated and real benchmark data sets shows clear and convincing advantages of our proposed method over the state-of-the-art methods.
