COID-FDCM: the fuzzy maintained dendritic cell classification method

Authors:
Zeineb Chelly;Abir Smiti;Zied Elouedi
Affiliations:
LARODEC, University of Tunis, Higher Institute of Management of Tunis, Bardo, Tunisia;LARODEC, University of Tunis, Higher Institute of Management of Tunis, Bardo, Tunisia;LARODEC, University of Tunis, Higher Institute of Management of Tunis, Bardo, Tunisia
Venue:
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
Year:
2012

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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.