An improved multi-label classification method and its application to functional genomics

Benhui Chen, Weifeng Gu, Jinglu Hu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


In this paper, a multi-label classification method based on label ranking and delicate boundary Support Vector Machine (SVM) is proposed for solving the functional genomics applications. Firstly, an improved probabilistic SVM with delicate decision boundary is used as scoring approach to obtain a proper label rank. Secondly, an instance-dependent thresholding strategy is proposed to decide classification results. A d-folds validation approach is utilised to determine a set of target thresholds for all training samples as teachers, then an appropriate instance-dependent threshold for each testing instance is obtained by applying k-Nearest Neighbours (KNN) strategy on this teacher threshold set.

Original languageEnglish
Pages (from-to)133-145
Number of pages13
JournalInternational journal of computational biology and drug design
Issue number2
Publication statusPublished - 2010 Sept


  • Functional genomics
  • Multi-label classification
  • Ranking based method
  • SVM
  • Support vector machine
  • Thresholding strategy

ASJC Scopus subject areas

  • Drug Discovery
  • Computer Science Applications


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