Sparse Distance Weighted Discrimination

TL;DR

This paper introduces an efficient algorithm for sparse distance weighted discrimination (DWD) suitable for high-dimensional data, improving computational speed and classification accuracy over existing methods.

Contribution

It develops a novel, fast algorithm for sparse penalized DWD and provides an R package, addressing computational challenges in high-dimensional classification.

Findings

Algorithm significantly reduces computation time.

Method achieves competitive classification accuracy.

Extensive experiments validate efficiency and effectiveness.

Abstract

Distance weighted discrimination (DWD) was originally proposed to handle the data piling issue in the support vector machine. In this paper, we consider the sparse penalized DWD for high-dimensional classification. The state-of-the-art algorithm for solving the standard DWD is based on second-order cone programming, however such an algorithm does not work well for the sparse penalized DWD with high-dimensional data. In order to overcome the challenging computation difficulty, we develop a very efficient algorithm to compute the solution path of the sparse DWD at a given fine grid of regularization parameters. We implement the algorithm in a publicly available R package sdwd. We conduct extensive numerical experiments to demonstrate the computational efficiency and classification performance of our method.