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The Effect of Binary Matching Rules in Negative Selection

  • Fabio González
  • Dipankar Dasgupta
  • Jonatan Gómez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2723)

Abstract

Negative selection algorithm is one of the most widely used techniques in the field of artificial immune systems. It is primarily used to detect changes in data/behavior patterns by generating detectors in the complementary space (from given normal samples). The negative selection algorithm generally uses binary matching rules to generate detectors. The purpose of the paper is to show that the low-level representation of binary matching rules is unable to capture the structure of some problem spaces. The paper compares some of the binary matching rules reported in the literature and study how they behave in a simple two-dimensional real-valued space. In particular, we study the detection accuracy and the areas covered by sets of detectors generated using the negative selection algorithm.

Keywords

False Alarm Rate Negative Selection Anomaly Detection Problem Space Binary String 
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 2003

Authors and Affiliations

  • Fabio González
    • 1
    • 2
  • Dipankar Dasgupta
    • 1
  • Jonatan Gómez
    • 1
    • 2
  1. 1.Division of Computer ScienceThe University of MemphisMemphis
  2. 2.Universidad Nacional de ColombiaBogotáColombia

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