Composite quantile‐based classifiers. (5th May 2020)

• Record Type: Journal Article
• Authors: Pritchard, David A.; Liu, Yufeng
• Is Part Of: Statistical analysis and data mining. Volume 13:Number 4(2020)
• Page Start: 337
• Page End: 353
• Publication Date: 2020-05-05
• Subjects: classification -- high‐dimensional data -- quantile‐based classifier -- supervised learning; Data mining -- Statistical methods -- Periodicals; 006.312
• DOI: 10.1002/sam.11460
• Languages: English
• ISSNs: 1932-1864

Abstract

Abstract: 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.