On Large Margin Hierarchical Classification With Multiple Paths

Junhui Wang, Xiaotong Shen, Wei Pan

Research output: Contribution to journalArticle

16 Scopus citations

Abstract

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 ψ-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 in the literature.

Original languageEnglish (US)
Pages (from-to)1213-1223
Number of pages11
JournalJournal of the American Statistical Association
Volume104
Issue number487
DOIs
StatePublished - 2009

Keywords

  • Directed acyclic graph
  • Functional genomics
  • Generalization
  • Structured learning
  • Tuning

Fingerprint Dive into the research topics of 'On Large Margin Hierarchical Classification With Multiple Paths'. Together they form a unique fingerprint.

  • Cite this