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A Scalable Artificial Immune System Model for Dynamic Unsupervised Learning

  • Olfa Nasraoui
  • Fabio Gonzalez
  • Cesar Cardona
  • Carlos Rojas
  • Dipankar Dasgupta
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2723)

Abstract

Artificial Immune System (AIS) models offer a promising approach to data analysis and pattern recognition. However, in order to achieve a desired learning capability (for example detecting all clusters in a dat set), current models require the storage and manipulation of a large network of B Cells (with a number often exceeding the number of data points in addition to all the pairwise links between these B Cells). Hence, current AIS models are far from being scalable, which makes them of limited use, even for medium size data sets.

We propose a new scalable AIS learning approach that exhibits superior learning abilities, while at the same time, requiring modest memory and computational costs. Like the natural immune system, the strongest advantage of immune based learning compared to current approaches is expected to be its ease of adaptation in dynamic environments. We illustrate the ability of the proposed approach in detecting clusters in noisy data.

Keywords

Artificial immune systems scalability clustering evolutionary computation dynamic learning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Olfa Nasraoui
    • 1
  • Fabio Gonzalez
    • 2
  • Cesar Cardona
    • 1
  • Carlos Rojas
    • 1
  • Dipankar Dasgupta
    • 2
  1. 1.Department of Electrical and Computer EngineeringThe University of MemphisMemphis
  2. 2.Division of Computer SciencesThe University of MemphisMemphis

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