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Quiet in class: classification, noise and the dendritic cell algorithm

Gu, Feng; Feyereisl, Jan; Oates, Robert; Reps, Jenna; Greensmith, Julie; Aickelin, Uwe

Authors

Feng Gu

Jan Feyereisl

Robert Oates

Jenna Reps

Julie Greensmith

Uwe Aickelin



Abstract

Theoretical analyses of the Dendritic Cell Algorithm (DCA) have yielded several criticisms about its underlying structure and operation. As a result, several alterations and fixes have been suggested in the literature to correct for these findings. A contribution of this work is to investigate the effects of replacing the classification stage of the DCA (which is known to be flawed) with a traditional machine learning technique. This work goes on to question the merits of those unique properties of the DCA that are yet to be thoroughly analysed. If none of these properties can be found to have a benefit over traditional approaches, then “fixing” the DCA is arguably less efficient than simply creating a new algorithm. This work examines the dynamic filtering property of the DCA and questions the utility of this unique feature for the anomaly detection problem. It is found that this feature, while advantageous for noisy, time-ordered classification, is not as useful as a traditional static filter for processing a synthetic dataset. It is concluded that there are still unique features of the DCA left to investigate. Areas that may be of benefit to the Artificial Immune Systems community are suggested.

Journal Article Type Article
Publication Date Sep 1, 2011
Journal Lecture Notes in Computer Science
Electronic ISSN 0302-9743
Publisher Humana Press
Peer Reviewed Peer Reviewed
Volume 6825
Institution Citation Gu, F., Feyereisl, J., Oates, R., Reps, J., Greensmith, J., & Aickelin, U. (2011). Quiet in class: classification, noise and the dendritic cell algorithm. Lecture Notes in Artificial Intelligence, 6825, doi:10.1007/978-3-642-22371-6_17
DOI https://doi.org/10.1007/978-3-642-22371-6_17
Publisher URL http://link.springer.com/chapter/10.1007%2F978-3-642-22371-6_17
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
Additional Information Published in: Artificial Immune Systems: 10th International Conference, ICARIS 2011, Cambridge, UK, July 18-21, 2011 : proceedings / P. Lio, G. Nicosia, and T. Stibor (Eds.). Berlin : Springer, 2011, p. 173-186. ISBN 9783642223709

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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