MAICS: Multilevel Artificial Immune Classification System

  • Michal Bereta
  • Tadeusz Burczynski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4029)


This paper presents a novel approach to feature selection and multiple-class classification problems. The proposed method is based on metaphors derived from artificial immune systems, clonal and negative selection paradigms. A novel clonal selection algorithm – Immune K-Means, is proposed. The proposed system is able to perform feature selection and model identification tasks by evolving specialized subpopulations of T- and B-lymphocytes. Multilevel evolution and real-valued coding enable for further extending of the proposed model and interpreting the subpopulations of lymphocytes as sets of evolving fuzzy rules.


Feature Selection Fuzzy Rule Negative Selection Binary String Clonal Selection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Michal Bereta
    • 1
  • Tadeusz Burczynski
    • 1
    • 2
  1. 1.Institute of Computer Modeling, Artificial Intelligence DepartmentCracow University of TechnologyCracowPoland
  2. 2.Department for Strength of Materials and Computational MechanicsSilesian University of TechnologyGliwicePoland

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