Angle-Based Hierarchical Classification Using Exact Label Embedding

Yiwei Fan, Xiaoling Lu, Yufeng Liu and Junlong Zhao

Journal of the American Statistical Association, 2022, vol. 117, issue 538, 704-717

Abstract: 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. Supplementary materials for this article are available online.

Date: 2022
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DOI: 10.1080/01621459.2020.1801450

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