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Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Articulation and clarification of the dendritic cell algorithm
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
Quiet in class: classification, noise and the dendritic cell algorithm
ICARIS'11 Proceedings of the 10th international conference on Artificial immune systems
Fault detection in analog circuits using a fuzzy dendritic cell algorithm
ICARIS'11 Proceedings of the 10th international conference on Artificial immune systems
Rethinking concepts of the dendritic cell algorithm for multiple data stream analysis
ICARIS'12 Proceedings of the 11th international conference on Artificial Immune Systems
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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.