Abstract
The dendritic cell algorithm is often presented as an immune-inspired one class classifier. Recently the dendritic cell algorithm has been criticised as its current decision making stage has many serious mathematical flaws which bring into question its applicability in other areas. However, previous work has demonstrated that the algorithm has properties which make it robust to a certain source of uncertainty, specifically measurement noise. This paper presents a discussion about the role of uncertainty within classification tasks and goes on to identify the strengths and weaknesses of the dendritic cell algorithm from this perspective. By examining other techniques for protecting against uncertainty, future directions for the dendritic cell algorithm are identified and discussed.
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Oates, R., Kendall, G., Garibaldi, J.M. (2010). Classifying in the Presence of Uncertainty: A DCA Perspective. In: Hart, E., McEwan, C., Timmis, J., Hone, A. (eds) Artificial Immune Systems. ICARIS 2010. Lecture Notes in Computer Science, vol 6209. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14547-6_7
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DOI: https://doi.org/10.1007/978-3-642-14547-6_7
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