Application of the feature-detection rule to the negative selection algorithm

dc.contributor.authorPoggiolini, Mario
dc.contributor.authorEngelbrecht, Andries P.
dc.contributor.emailengel@cs.up.ac.zaen_US
dc.date.accessioned2014-03-26T07:52:17Z
dc.date.available2014-03-26T07:52:17Z
dc.date.issued2013-08
dc.description.abstractThe Negative Selection Algorithm developed by Forrest et al. was inspired by the way in which T-cell lymphocytes mature within the thymus before being released into the blood system. The mature T-cell lymphocytes exhibit an interesting characteristic, in that they are only activated by non-self cells that invade the human body. The Negative Selection Algorithm utilises an affinity matching function to ascertain whether the affinity between a newly generated (NSA) T-cell lymphocyte and a self-cell is less than a particular threshold; that is, whether the T-cell lymphocyte is activated by the self-cell. T-cell lymphocytes not activated by self-sells become mature T-cell lymphocytes. A new affinity matching function termed the feature-detection rule is introduced in this paper. The feature-detection rule utilises the interrelationship between both adjacent and non-adjacent features of a particular problem domain to determine whether an antigen is activated by an artificial lymphocyte. The performance of the featuredetection rule is contrasted with traditional affinity matching functions, currently employed within Negative Selection Algorithms, most notably the r-chunks rule (which subsumes the r-contiguous bits rule) and the hamming distance rule. This paper shows that the feature-detection rule greatly improves the detection rates and false alarm rates exhibited by the NSA (utilising the r-chunks and hamming distance rule) in addition to refuting the way in which permutation masks are currently being applied in artificial immune systems.en_US
dc.description.urihttp://www.elsevier.com/locate/eswaen_US
dc.identifier.citationPoggiolini, M & Engelbrecht, A 2013, 'Application of the feature-detection rule to the negative selection algorithm', Expert Systems with Applications, vol. 40, no. 8, pp. 3001-3014.en_US
dc.identifier.issn0957-4174 (print)
dc.identifier.issn1873-6793 (online)
dc.identifier.other10.1016/j.eswa.2012.12.016
dc.identifier.urihttp://hdl.handle.net/2263/37140
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.rights© 2013 Elsevier. All rights reserved. Notice : this is the author’s version of a work that was accepted for publication in Expert Systems with Applications .Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Expert Systems with Applications , vol. 40,no. 8, 2013. doi. : 10.1016/j.eswa.2012.12.016en_US
dc.subjectArtificial immune systemsen_US
dc.subjectAffinity matching functionsen_US
dc.subjectNegative selectionen_US
dc.subjectFeature selectionen_US
dc.titleApplication of the feature-detection rule to the negative selection algorithmen_US
dc.typePostprint Articleen_US

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Poggiolini_Application_2013.pdf
Size:
409.2 KB
Format:
Adobe Portable Document Format
Description:
Postprint Article

License bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description: