Optimal combination of feature selection and classification via local hyperplane based learning strategy.

Journal: BMC bioinformatics
Published Date: Jul 10, 2015

Abstract

BACKGROUND: Classifying cancers by gene selection is among the most important and challenging procedures in biomedicine. A major challenge is to design an effective method that eliminates irrelevant, redundant, or noisy genes from the classification, while retaining all of the highly discriminative genes.

Authors

  • Xiaoping Cheng
    School of Computer Science& Engineering, South China University of Technology, Guangdong, China. c.xp01@mail.scut.edu.cn.
  • Hongmin Cai
    School of Computer Science& Engineering, South China University of Technology, Guangdong, China. hmcai@scut.edu.cn.
  • Yue Zhang
    Department of Ophthalmology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
  • Bo Xu
    State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China.
  • Weifeng Su
    BNU-HKBU United International College, Hong Kong, China. wfsu@uic.edu.hk.

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