On Large Margin Hierarchical Classification With Multiple Paths

• 成果类型: Article
• 署名作者: Wang, Junhui; Shen, Xiaotong; Pan, Wei
• 署名单位: University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; University of Minnesota System; University of Minnesota Twin Cities; University of Minnesota System; University of Minnesota Twin Cities
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/jasa.2009.tm08084
• 发表日期: 2009
• 页码: 1213-1223
• 关键词: vector; algorithms; regression

摘要

Hierarchical classification is critical to knowledge management and exploration. as is gene function prediction and document categorization. In hierarchical classification, an input is classified according to a structured hierarchy. In such a situation, the central issue is how to effectively utilize the interclass relationship to improve the generalization performance of flat classification ignoring such dependency. In this article, we propose a novel large margin method through constraints characterizing a multipath hierarchy, where class membership can be nonexclusive. The proposed method permits a treatment of various losses for hierarchical classification. For implementation. we focus on the symmetric difference loss and two large margin classifiers: support vector machines and psi-learning. Finally, theoretical and numerical analyses are conducted, in addition to an application to gene function prediction. They suggest that the proposed method achieves the desired objective and outperforms strong competitors ill the literature.