TY - JOUR
T1 - Quiet in class: classification, noise and the dendritic cell algorithm
AU - Gu, Feng
AU - Feyereisl, Jan
AU - Oates, Robert
AU - Reps, Jenna
AU - Greensmith, Julie
AU - Aickelin, Uwe
N1 - Note: 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
PY - 2011/9/1
Y1 - 2011/9/1
N2 - 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.
AB - 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.
U2 - 10.1007/978-3-642-22371-6_17
DO - 10.1007/978-3-642-22371-6_17
M3 - Article
SN - 0302-9743
VL - 6825
SP - 173
EP - 186
JO - Lecture Notes in Computer Science
JF - Lecture Notes in Computer Science
T2 - 10th International Conference on Artificial Immune Systems (ICARIS 2011)
Y2 - 18 July 2011 through 21 July 2011
ER -