Title page for ETD etd-06262009-112502

Document Type Master's Dissertation
Author Poggiolini, Mario
Email mpoggiolini@gmail.com
URN etd-06262009-112502
Document Title The feature detection rule and its application within the negative selection algorithm
Degree MSc
Department Computer Science
Advisor Name Title
Prof A P Engelbrecht Supervisor
  • negative selection algorithm
  • artificial immune systems
  • computational intelligence
Date 2009-04-20
Availability unrestricted

The negative selection algorithm developed by Forrest et al. was inspired by the manner in which T-cell lymphocytes mature within the thymus before being released into the blood system. The resultant T-cell lymphocytes, which are then released into the blood, exhibit an interesting characteristic: they are only activated by non-self cells that invade the human body. The work presented in this thesis examines the current body of research on the negative selection theory and introduces a new affinity threshold function, called the feature-detection rule. The feature-detection rule utilises the inter-relationship between both adjacent and non-adjacent features within a particular problem domain to determine if an artificial lymphocyte is activated by a particular antigen. The performance of the feature-detection rule is contrasted with traditional affinity-matching functions currently employed within negative selection theory, most notably the r-chunks rule (which subsumes the r-contiguous bits rule) and the hamming-distance rule. The performance will be characterised by considering the detection rate, false-alarm rate, degree of generalisation and degree of overfitting. The thesis will show that the feature-detection rule is superior to the r-chunks rule and the hamming-distance rule, in that the feature-detection rule requires a much smaller number of detectors to achieve greater detection rates and less false-alarm rates. The thesis additionally refutes that the way in which permutation masks are currently applied within negative selection theory is incorrect and counterproductive, while placing the feature-detection rule within the spectrum of affinity-matching functions currently employed by artificial immune-system (AIS) researchers.

ŠUniversity of Pretoria 2008

Please cite as follows

Poggiolini, M 2008, The feature detection rule and its application within the negative selection algorithm, MSc dissertation, University of Pretoria, Pretoria, viewed yymmdd < http://upetd.up.ac.za/thesis/available/etd-06262009-112502/ >

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