• DocumentCode
    2955311
  • Title

    A Novel Immune Algorithm for Supervised Classification Problem

  • Author

    Li, Xiaoming

  • Author_Institution
    Modern Educ. Center (MEC), HeNan Radio & Telev. Univ., Zhengzhou, China
  • fYear
    2011
  • fDate
    30-31 July 2011
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    This article presents a novel immune algorithm for a solution of supervised classification problem .The algorithm is based on the risk model, the use of dangerous and hazardous signal mechanism, the risk by assessing the Antigen to the signal classification; and use of antibody-Antigen interactions learning mechanisms to make antibodies have strong populations of adaptive learning capacity. Simulation results show that the algorithm have good classification results and learning performance compared with other traditional algorithm.
  • Keywords
    artificial immune systems; learning (artificial intelligence); pattern classification; adaptive learning capacity; antibody-antigen interactions learning mechanisms; dangerous signal mechanism; hazardous signal mechanism; immune algorithm; learning performance; risk model; signal classification; supervised classification problem; Classification algorithms; Cloning; Complexity theory; Data models; Immune system; Joining processes; Training;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control, Automation and Systems Engineering (CASE), 2011 International Conference on
  • Conference_Location
    Singapore
  • Print_ISBN
    978-1-4577-0859-6
  • Type

    conf

  • DOI
    10.1109/ICCASE.2011.5997726
  • Filename
    5997726