Composite quantile‐based classifiers
Composite quantile‐based classifiers
复制标题
基于复合分位数的分类器
DOI：
10.1002/sam.11460
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Liu, Yufeng
中科院分区：
文献类型：
--
作者：
Pritchard, David A.;Liu, Yufeng
高维数据的准确分类在许多科学应用中都很重要。我们提出了一类高维分类方法，该方法基于样本的特征向量到类内总体分位数的按分量距离的比较。这些方法的动机是基于这些分量距离的分位数分类器是用于最佳选择分位数级别的最强大的单变量分类器。提出了一种基于这些分量到类内分位数的距离来构造多变量分类器的简单聚集方法。结果表明，随着样本量的增加，该分类器与渐近最优分类器是一致的。我们提出的分类器产生简单的分段线性决策规则边界，这些边界可以被有效地训练。数值结果表明，所提出的分类器在模拟数据和基准电子邮件应用程序上的性能具有竞争力。
Accurate classification of high‐dimensional data is important in many scientific applications. We propose a family of high‐dimensional classification methods based upon a comparison of the component‐wise distances of the feature vector of a sample to the within‐class population quantiles. These methods are motivated by the fact that quantile classifiers based on these component‐wise distances are the most powerful univariate classifiers for an optimal choice of the quantile level. A simple aggregation approach for constructing a multivariate classifier based upon these component‐wise distances to the within‐class quantiles is proposed. It is shown that this classifier is consistent with the asymptotically optimal classifier as the sample size increases. Our proposed classifiers result in simple piecewise‐linear decision rule boundaries that can be efficiently trained. Numerical results are shown to demonstrate competitive performance for the proposed classifiers on both simulated data and a benchmark email spam application.
影响因子：
3.1
作者：
Li,Ran;Wu,Baolin
通讯作者：
Wu,Baolin