Hierarchical multi-label classification with SVMs: A case study in gene function prediction

Peerapon Vateekul, Miroslav Kubat, Kanoksri Sarinnapakorn

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


Hierarchical multi-label classification is a relatively new research topic in the field of classifier induction. What distinguishes it from earlier tasks is that it allows each example to belong to two or more classes at the same time, and by assuming that the classes are mutually related by generalization/specialization operators. The paper first investigates the problem of performance evaluation in these domains. After this, it proposes a new induction system, HR-SVM, built around support vector machines. In our experiments, we demonstrate that this system's performance compares favorably with that earlier attempts, and then we proceed to an investigation of how HR-SVM's individual modules contribute to the overall system's behavior. As a testbed, we use a set of benchmark domains from the field of gene-function prediction.

Original languageEnglish (US)
Pages (from-to)717-738
Number of pages22
JournalIntelligent Data Analysis
Issue number4
StatePublished - 2014


  • gene-function prediction
  • Hierarchical multi-label classification
  • support vector machines

ASJC Scopus subject areas

  • Artificial Intelligence
  • Theoretical Computer Science
  • Computer Vision and Pattern Recognition


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