High dimensional classification has been highlighted for last two decades and much research has been conducted in order to circumvent challenges encountered in high dimensions. While existing methods have focused mainly on developing classification rules assuming independence of covariates or using regularization on the sample covariance matrix or the sample mean vector or among others, we propose a novel approach that employs the "discriminatory power" of each covariate, selects a set of important variables yielding the lowest misclassification rate empirically, and constructs the optimal linear classifier with selected variables. We carry out simulation studies and analyze real data sets to illustrate the performance of our proposed classifier by comparing it with existing classifiers.

翻译：在过去二十年中,人们一直强调高维分类,并进行了许多研究,以规避在高维方面所遇到的挑战;虽然现有方法主要侧重于制定分类规则,假设共变体独立,或使用样本共变矩阵或样本中平均矢量的正规化,或者除其他外,我们建议采用新颖的方法,利用每个共变体的“歧视性力量”,从经验上选择一系列重要的变量,得出最低的错误分类率,并根据选定的变量构建最佳线性分类器。我们进行模拟研究,分析真实的数据集,通过将拟议分类器与现有的分类器进行比较,来说明我们提议的分类器的性能。