Pairwise Variable Selection for Classification

• Activity Number: 136
• Type: Contributed
• Date/Time: Monday, August 2, 2010 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistical Computing
• Author(s): Xingye Qiao*+ and Yufeng Liu and J. S. Marron
• Companies: The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill
• Keywords: Classification ; False Discovery Rate ; Fisher linear discrimination ; High-dimensional, low-sample size data ; Variable Selection

Abstract

While traditional marginal variable selection methods have the merits of convenient implementation and good interpretability, they do not take the joint effects among variables into account. In some situations, variables which have strong joint effects can be passed over by marginal methods because of their small marginal effects. In the context of binary classification in supervised learning, we develop a novel method of pairwise variable selection, based on a within-class permutation test to evaluate the statistical significance of joint effects. Moreover, we introduce a new notion of variable selection quality, bivariate False Discovery Rate (biFDR), and provide an estimation procedure for biFDR. A simulated example and a real data application are analyzed to demonstrate the usefulness of the proposed approach.