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An Improved Artificial Immune Recognition System Based on the Average Scatter Matrix Trace Criterion

  • Xiaoyang Fu
  • Shuqing Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7331)

Abstract

This paper proposed an improved artificial immune recognition system (IAIRS) based on the average scatter matrix trace (ASMT) criterion. In essence, the artificial immune recognition system (AIRS) is an evolving algorithm. Through clonal expansion, affinity maturation, resource competition and immune memory etc, a set of new samples (memory cells) is produced. The ASMT of memory cells will be decreased and the minimized ASMT can be as the optimal criterion of AIRS. The IAIRS algorithm is demonstrated on a number of benchmark data sets effectively.

Keywords

artificial immune recognition system scatter matrix trace pattern classification 

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References

  1. 1.
    Timmis, J., Neal, M.: A Resource limited Artificial Immune System. Knowledge Based Systems 14(3/4), 121–130 (2001)CrossRefGoogle Scholar
  2. 2.
    Watkins, A., Boggess, L.: A New Classifier based on Resource Limited Artificial Immune System. In: Ebherhart, R. (ed.) Congress on Evolutionary Computation. Part of the World Congress on Computational Intelligence, Honoluu, HI, pp. 1546–1551. IEEE, Piscataway (2002)Google Scholar
  3. 3.
    Watkins, A., Timmis, J.: Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm. Kluwer Academic Publisher, Netherland (2003)Google Scholar
  4. 4.
    Zhang, L., Zhong, Y., Huang, B., Li, P.: A Resource Limited Artificial Immune System Algorithm for Supervised Classification of Multi/hyper-spectral Remote Sensing Imagery. International Journal of Remote Sensing 28(7-8), 1665–1686 (2007)CrossRefGoogle Scholar
  5. 5.
    Fu, X., Zhang, S., Pang, Z.: A Resource Limited Immune Approach for Evolving Architecture and Weights of Multilayer Neural Network. In: Tan, Y., Shi, Y., Tan, K.C. (eds.) ICSI 2010, Part I. LNCS, vol. 6145, pp. 328–337. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. John Wiley & Sons, Beijing (2001) ISBN:0-471-05669-3zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Xiaoyang Fu
    • 1
  • Shuqing Zhang
    • 2
  1. 1.Department of Computer Science and TechnologyZhuhai College of Jilin UniversityZhuhaiChina
  2. 2.Northeast Institute of Geography and AgroecologyChinese Academy of SciencesChangchunChina

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