This Title All WIREs
How to cite this WIREs title:
WIREs Comp Stat

Distance‐weighted discrimination

Full article on Wiley Online Library:   HTML PDF

Can't access this content? Tell your librarian.

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
Simulated example illustrating data piling in high dimensions, and how it is mitigated by DWD. Each plot shows projection scores on a particular direction in the space (with common axes for direct comparison, as indicated by the titles). Angles to the optimal in degrees are shown for each other direction, indicating superior generalizability for DWD.
[ Normal View | Magnified View ]

Browse by Topic

Statistical and Graphical Methods of Data Analysis > Statistical Graphics and Visualization
Applications of Computational Statistics > Genomics/Proteomics/Genetics
Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification

Access to this WIREs title is by subscription only.

Recommend to Your
Librarian Now!

The latest WIREs articles in your inbox

Sign Up for Article Alerts