Abstract
The Dendritic Cell Algorithm (DCA) is an immune-inspired algorithm based on the behavior of natural dendritic cells. The DCA, as a binary classifier, classifies in a crisp manner each data item as either normal or anomalous. However, it was shown that DCA is sensitive to the input class data order. This problem was solved by the development of the fuzzy dendritic cell algorithm. The performance of the latter algorithm relies on its parameters tuning as this process is based on the use of a fuzzy clustering technique. We, thus, believe that the choice of the right fuzzy clustering technique is crucial for the system. In this paper, we try to review the fuzzy version of DCA and to investigate its performance when hybridized with different fuzzy clustering techniques. The aim of this hybridization is to select the most appropriate fuzzy clustering approach in order to generate an overall automated robust fuzzy DCA classifier.
Published Version
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