Quiet in class: classification, noise and the dendritic cell algorithm
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 e...
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| Format: | Article |
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Springer
2011
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| Online Access: | https://eprints.nottingham.ac.uk/34130/ |
| _version_ | 1848794780111208448 |
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| author | Gu, Feng Feyereisl, Jan Oates, Robert Reps, Jenna Greensmith, Julie Aickelin, Uwe |
| author_facet | Gu, Feng Feyereisl, Jan Oates, Robert Reps, Jenna Greensmith, Julie Aickelin, Uwe |
| author_sort | Gu, Feng |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:21:38Z |
| format | Article |
| id | nottingham-34130 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:21:38Z |
| publishDate | 2011 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-341302020-05-04T16:30:56Z https://eprints.nottingham.ac.uk/34130/ Quiet in class: classification, noise and the dendritic cell algorithm Gu, Feng Feyereisl, Jan Oates, Robert Reps, Jenna Greensmith, Julie Aickelin, Uwe 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. Springer 2011-09-01 Article PeerReviewed Gu, Feng, Feyereisl, Jan, Oates, Robert, Reps, Jenna, Greensmith, Julie and Aickelin, Uwe (2011) Quiet in class: classification, noise and the dendritic cell algorithm. Lecture Notes in Computer Science, 6825 . pp. 173-186. ISSN 0302-9743 http://link.springer.com/chapter/10.1007%2F978-3-642-22371-6_17 doi:10.1007/978-3-642-22371-6_17 doi:10.1007/978-3-642-22371-6_17 |
| spellingShingle | Gu, Feng Feyereisl, Jan Oates, Robert Reps, Jenna Greensmith, Julie Aickelin, Uwe Quiet in class: classification, noise and the dendritic cell algorithm |
| title | Quiet in class: classification, noise and the dendritic cell algorithm |
| title_full | Quiet in class: classification, noise and the dendritic cell algorithm |
| title_fullStr | Quiet in class: classification, noise and the dendritic cell algorithm |
| title_full_unstemmed | Quiet in class: classification, noise and the dendritic cell algorithm |
| title_short | Quiet in class: classification, noise and the dendritic cell algorithm |
| title_sort | quiet in class: classification, noise and the dendritic cell algorithm |
| url | https://eprints.nottingham.ac.uk/34130/ https://eprints.nottingham.ac.uk/34130/ https://eprints.nottingham.ac.uk/34130/ |