• DocumentCode
    478563
  • Title

    Artificial Immune Recognition System for DNA Microarray Data Analysis

  • Author

    Chen, Chuanliang ; Xu, Chuan ; Bie, Rongfang ; Gao, X.Z.

  • Author_Institution
    Coll. of Inf. Sci. & Technol, Beijing Normal Univ., Beijing
  • Volume
    6
  • fYear
    2008
  • fDate
    18-20 Oct. 2008
  • Firstpage
    633
  • Lastpage
    637
  • Abstract
    Artificial immune systems (AIS) are emerging information processing methods, which embody the principles of biological immune systems for tackling complex realworld problems. The artificial immune recognition system (AIRS) is a new kind of supervised learning AIS. The development of microarray technology has supplied a large volume of data for the prediction and diagnosis of cancer. Many popular machine learning techniques have been used in the microarray data analysis. In this paper, we apply AIRS to perform the microarray data classification based on an improved version of the information gain feature selection method. Three traditional classifiers have also been employed in our experiments for performance comparison. The results demonstrate the promising ability of AIRS in the microarray data analysis.
  • Keywords
    artificial immune systems; cancer; data analysis; feature extraction; lab-on-a-chip; learning (artificial intelligence); pattern classification; DNA microarray data analysis; artificial immune recognition system; biological immune systems; cancer diagnosis; feature selection method; machine learning; microarray data classification; supervised learning; Artificial immune systems; Biology computing; Cancer; DNA computing; Data analysis; Entropy; Immune system; Information processing; Information science; Performance gain; Artificial Immune Recognition System; Artificial Immune Systems; DNA Microarray Data Analysis; Natural Computation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Natural Computation, 2008. ICNC '08. Fourth International Conference on
  • Conference_Location
    Jinan
  • Print_ISBN
    978-0-7695-3304-9
  • Type

    conf

  • DOI
    10.1109/ICNC.2008.145
  • Filename
    4667912