Multiclass Distance-Weighted Discrimination

作者：Huang Hanwen^*; Liu Yufeng; Du Ying; Perou Charles M; Hayes D Neil; Todd Michael J; Marron J S

来源：Journal of Computational and Graphical Statistics, 2013, 22(4): 953-969.

DOI：10.1080/10618600.2012.700878

• Classification
• Fisher consistency
• Optimization

In this article, we extend the binary distance-weighted discrimination (DWD) to the multiclass case. In addition to the usual extensions that combine several binary DWD classifiers, we propose a global multiclass DWD (MDWD) that finds a single classifier that considers all classes at once. Our theoretical results show that MDWD is Fisher consistent, even in the particularly challenging case when there is no dominating class, that is, a class with probability bigger than 0.5. The performance of different multiclass DWD methods is assessed through simulation studies and application to real microarray datasets. Comparison with the support vector machines is also provided. MATLAB implementation of the proposed methods is given in the online supplementary materials.

• 出版日期2013-12