dc.contributor.advisor |
Engelbrecht, Andries P. |
|
dc.contributor.postgraduate |
Poggiolini, Mario |
en |
dc.date.accessioned |
2013-09-07T01:04:30Z |
|
dc.date.available |
2009-06-29 |
en |
dc.date.available |
2013-09-07T01:04:30Z |
|
dc.date.created |
2009-04-20 |
en |
dc.date.issued |
2009-06-29 |
en |
dc.date.submitted |
2009-06-26 |
en |
dc.description |
Dissertation (MSc)--University of Pretoria, 2009. |
en |
dc.description.abstract |
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. |
en |
dc.description.availability |
Unrestricted |
en |
dc.description.department |
Computer Science |
en |
dc.identifier.citation |
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://hdl.handle.net/2263/25866 > |
en |
dc.identifier.other |
E1306/gm |
en |
dc.identifier.upetdurl |
http://upetd.up.ac.za/thesis/available/etd-06262009-112502/ |
en |
dc.identifier.uri |
http://hdl.handle.net/2263/25866 |
|
dc.language.iso |
|
en |
dc.publisher |
University of Pretoria |
en_ZA |
dc.rights |
©University 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/ > E1306/ |
en |
dc.subject |
Negative selection algorithm |
en |
dc.subject |
Artificial immune systems |
en |
dc.subject |
Computational intelligence |
en |
dc.subject |
UCTD |
en_US |
dc.title |
The feature detection rule and its application within the negative selection algorithm |
en |
dc.type |
Dissertation |
en |