 |
    |
The Effect of Binary Matching Rules in Negative Selection
Fabio González1, Dipankar Dasgupta2, and Jonatan Gómez1
1Division of Computer Science,
The University of Memphis,
MemphisTN 38152
and Universidad Nacional de Colombia,
Bogotá, Colombia
{fgonzalz,jgomez}@memphis.edu
2Division of ComputerScience,
The University of Memphis,
Memphis TN 38152
dasgupta@memphis.edu
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.
LNCS 2723, p. 195 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2003