New feature-extraction criteria and classification algorithms for cancer gene expression datasets

Wang Shitong, Fu Lai Korris Chung, Deng Zhaohong, L. I.N. Qing, H. U. Dewen

Research output: Journal article publicationJournal articleAcademic researchpeer-review


In this study, based on DCCM (Differential Capability Control Machine), the new feature-extraction criterion NFEC is developed using the first-order differential information and a new feature-extraction algorithm DCCFE is accordingly proposed for binary classification problems. NFEC and DCCFE are then extended to their multi-classification versions, i.e., m_NFEC and m_DCCFE, respectively. Present experimental results demonstrate that the new feature extraction criteria and algorithms outperform or have comparable performance with the current methods for cancer gene expression datasets. Furthermore, since the new algorithms here admit more general first-order differential functions as the basis functions instead of kernel functions in SVM-based method, they perhaps have more potential applications in bioinformatics in the future.
Original languageEnglish
Pages (from-to)163-172
Number of pages10
Issue number3
Publication statusPublished - 1 Jan 2005


  • Bioinformatics
  • Cancer gene expression datasets
  • Classification
  • Differential capability control machine
  • Feature extraction

ASJC Scopus subject areas

  • General Biochemistry,Genetics and Molecular Biology


Dive into the research topics of 'New feature-extraction criteria and classification algorithms for cancer gene expression datasets'. Together they form a unique fingerprint.

Cite this