title: Bias-corrected diagonal discriminant rules for high-dimensional classification.
abstract: Diagonal discriminant rules have been successfully used for high-dimensional classification problems, but suffer from the serious drawback of biased discriminant scores. In this article, we propose improved diagonal discriminant rules with bias-corrected discriminant scores for high-dimensional classification. We show that the proposed discriminant scores dominate the standard ones under the quadratic loss function. Analytical results on why the bias-corrected rules can potentially improve the predication accuracy are also provided. Finally, we demonstrate the improvement of the proposed rules over the original ones through extensive simulation studies and real case studies.
authors.affiliation: Program of Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut 06520, USA. song.huang@yale.edu
authors.forename: Song
authors.initials: S
authors.lastname: Huang
authors.forename: Tiejun
authors.initials: T
authors.lastname: Tong
authors.forename: Hongyu
authors.initials: H
authors.lastname: Zhao
mesh_terms: D001185:Artificial Intelligence; D015982:Bias; D002965:Classification