Multiclass Distance-Weighted Discrimination
Multiclass Distance-Weighted Discrimination
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DOI：
10.1080/10618600.2012.700878
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发表时间：
2013-12-01
影响因子：
2.4
通讯作者：
Marron, J. S.
中科院分区：
文献类型：
--
作者：
Huang, Hanwen;Liu, Yufeng;Marron, J. S.
在这篇文章中，我们扩展的二元距离加权歧视（DWD）的多类的情况下。除了通常的扩展，结合联合收割机几个二进制DWD分类，我们提出了一个全球性的多类DWD（MDWD），找到一个单一的分类器，认为所有类在一次。我们的理论结果表明，MDWD是Fisher一致的，即使在特别具有挑战性的情况下，当没有主导类，也就是说，一个类的概率大于0.5。不同的多类DWD方法的性能进行评估，通过模拟研究和应用到真实的微阵列数据集。还提供了与支持向量机的比较。在线补充材料中给出了所提出方法的MATLAB实现。
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.