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Geometrical insights into the dendritic cell algorithm
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Genetic And Evolutionary Computation Conference archive
Proceedings of the 11th Annual conference on Genetic and evolutionary computation table of contents
Montreal, Québec, Canada
SESSION: Track 11: genetics-based machine learning table of contents
Pages 1275-1282  
Year of Publication: 2009
ISBN:978-1-60558-325-9
Authors
Thomas Stibor  Technische Universitaet Muenchen, Munich, Germany
Robert Oates  The University of Nottingham, Nottingham, United Kingdom
Graham Kendall  The University of Nottingham, Nottingham, United Kingdom
Jonathan M. Garibaldi  The University of Nottingham, Nottingham, United Kingdom
Sponsors
SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

This work examines the dendritic cell algorithm (DCA) from a mathematical perspective. By representing the signal processing phase of the algorithm using the dot product it is shown that the signal processing element of the DCA is actually a collection of linear classifiers. It is further shown that the decision boundaries of these classifiers have the potentially serious drawback of being parallel, severely limiting the applications for which the existing algorithm can be potentially used on. These ideas are further explored using artificially generated data and a novel visualisation technique that allows an entire population of dendritic cells to be inspected as a single classifier. The paper concludes that the applicability of the DCA to more complex problems is highly limited.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Y. Al-Hammadi, U. Aickelin, and J. Greensmith. Dca for bot detection. In Proceedings of the IEEE World Congress on Computational Intelligence (WCCI) Hong Kong, pages 1807--1816, 2008.
 
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J. Greensmith. The Dendritic Cell Algorithm. PhD thesis, The University of Nottingham, Computer Science, Jubilee Campus, Wollaton Road, Nottingham, NG8 1BB, 2007.
 
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J. Greensmith, U. Aickelin, and J. Twycross. Articulation and clarification of the dendric cell algorithm. In Proceedings of the 5th International Conference on Artificial Immune Systems (ICARIS 2006), pages 404--417, 2006.
 
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J. Greensmith, J. Feyereisl, and U. Aickelin. The dca: Some comparison: a comparative study between two biologically-inspired algorithms. Evolutionary Intelligence, 1(2):85--112, June 2008.
 
7
 
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G. James. Modern Engineering Mathematics. Pearson Education Limited, 4th edition, 2008.
 
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P. Matzinger. Tolerance, danger, and the extended family. Annual Review of Immunology, 12(1):991--1045, 1994.
 
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P. Matzinger. Friendly and dangerous signals: is the tissue in control? Nature Immunology, 8(1):11--13, 2007.
 
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R. Oates, G. Kendall, and J. Garibaldi. Frequency analysis for dendritic cell population tuning. Evolutionary Intelligence, 1(2):145--157, June 2008.
 
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Collaborative Colleagues:
Thomas Stibor: colleagues
Robert Oates: colleagues
Graham Kendall: colleagues
Jonathan M. Garibaldi: colleagues