Angle-Based Hierarchical Classification Using Exact Label Embedding
成果类型：
Article
署名作者：
Fan, Yiwei; Lu, Xiaoling; Liu, Yufeng; Zhao, Junlong
署名单位：
Renmin University of China; University of North Carolina; University of North Carolina Chapel Hill; Beijing Normal University
刊物名称：
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
ISSN/ISSBN：
0162-1459
DOI：
10.1080/01621459.2020.1801450
发表日期：
2022
页码：
704-717
关键词：
摘要：
Hierarchical classification problems are commonly seen in practice. However, most existing methods do not fully use the hierarchical information among class labels. In this article, a novel label embedding approach is proposed, which keeps the hierarchy of labels exactly, and reduces the complexity of the hypothesis space significantly. Based on the newly proposed label embedding approach, a new angle-based classifier is developed for hierarchical classification. Moreover, to handle massive data, a new (weighted) linear loss is designed, which has a closed form solution and is computationally efficient. Theoretical properties of the new method are established and intensive numerical comparisons with other methods are conducted. Both simulations and applications in document categorization demonstrate the advantages of the proposed method.for this article are available online.