Abstract
The Dendritic Cell Algorithm (DCA) is an immune-inspired classification algorithm based on the behavior of natural dendritic cells (DC). A major problem with DCA is that it is sensitive to the data order. This limitation is due to the existence of noisy or redundant data and to the crisp separation between the DC semi-mature context and the DC mature context. This paper proposes a novel immune-inspired alleviated model of the DCA grounded in fuzzy set theory and a maintenance database method. Our new model focuses on smoothing the crisp separation between the two DCs’ contexts using fuzzy set theory. A maintenance database approach is used as well to guarantee the quality of the DCA database. Experiments are provided to show that our method performs much better than the standard DCA in terms of classification accuracy.
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Chelly, Z., Smiti, A., Elouedi, Z. (2012). COID-FDCM: The Fuzzy Maintained Dendritic Cell Classification Method. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7268. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29350-4_28
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DOI: https://doi.org/10.1007/978-3-642-29350-4_28
Publisher Name: Springer, Berlin, Heidelberg
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