| Geometrical insights into the dendritic cell algorithm |
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Genetic And Evolutionary Computation Conference
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Proceedings of the 11th Annual conference on Genetic and evolutionary computation
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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
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Downloads (6 Weeks): 12, Downloads (12 Months): 37, Citation Count: 0
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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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