Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
The feature detection rule and its application within the negative selection algorithm
Dissertation (MSc)--University of Pretoria, 2009.
Saved in:
| Other Authors: | |
|---|---|
| Format: | Thesis |
| Published: |
University of Pretoria
2013
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| _version_ | 1867613674468802560 |
|---|---|
| access_status_str | Open Access |
| author2 | Engelbrecht, Andries P. |
| author_browse | Engelbrecht, Andries P. |
| author_facet | Engelbrecht, Andries P. |
| collection | Thesis |
| dc_rights_str_mv | ©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/ |
| description | Dissertation (MSc)--University of Pretoria, 2009. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/25866 |
| institution | University of Pretoria (South Africa) |
| last_indexed | 2026-06-10T12:39:54.193Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2013 |
| publishDateRange | 2013 |
| publishDateSort | 2013 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/25866 The feature detection rule and its application within the negative selection algorithm Engelbrecht, Andries P. mpoggiolini@gmail.com Poggiolini, Mario Negative selection algorithm Artificial immune systems Computational intelligence UCTD Dissertation (MSc)--University of Pretoria, 2009. 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. Computer Science Unrestricted 2013-09-07T01:04:30Z 2009-06-29 2013-09-07T01:04:30Z 2009-04-20 2009-06-29 2009-06-26 Dissertation 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 > E1306/gm http://hdl.handle.net/2263/25866 http://upetd.up.ac.za/thesis/available/etd-06262009-112502/ ©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/ application/pdf University of Pretoria |
| spellingShingle | Negative selection algorithm Artificial immune systems Computational intelligence UCTD The feature detection rule and its application within the negative selection algorithm |
| title | The feature detection rule and its application within the negative selection algorithm |
| title_full | The feature detection rule and its application within the negative selection algorithm |
| title_fullStr | The feature detection rule and its application within the negative selection algorithm |
| title_full_unstemmed | The feature detection rule and its application within the negative selection algorithm |
| title_short | The feature detection rule and its application within the negative selection algorithm |
| title_sort | feature detection rule and its application within the negative selection algorithm |
| topic | Negative selection algorithm Artificial immune systems Computational intelligence UCTD |
| url | http://hdl.handle.net/2263/25866 http://upetd.up.ac.za/thesis/available/etd-06262009-112502/ |