Distance‐weighted discrimination

Distance‐weighted discrimination is a classification (discrimination) method. Like the popular support vector machine, it is rooted in optimization; however, the underlying optimization problem is modified to give better generalizability, particularly in high dimensions. The two key ideas are that distance‐weighted discrimination directly targets the data piling problem and also correctly handles unknown, unbalanced subclasses in the data. A useful property of distance‐weighted discrimination, beyond just good classification performance, is that it provides a direction vector in high‐dimensional data space with several purposes, including indication of driving phenomena behind class differences, data visualization, and batch adjustment tasks. WIREs Comput Stat 2015, 7:109–114. doi: 10.1002/wics.1345 This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification Applications of Computational Statistics > Genomics/Proteomics/Genetics Statistical and Graphical Methods of Data Analysis > Statistical Graphics and Visualization