Search

The dendritic cell algorithm (DCA) is a classification algorithm based on the behavior of natural dendritic cells (DCs). In literature, DCA has given good classification results. However, DCA was known to be sensitive to the order of the instance classes. To solve this limitation, a fuzzy DCA version was developed stating that the cause of such sensitivity is related to the DCA crisp classification task (hypothesis 1). In this paper, we hypothesize that there is a second possible cause of such DCA sensitivity which is related to the possible existence of noisy instances presented in the DCA signal data set (hypothesis 2). Thus, we aim, first of all, to test the trueness of the latter hypothesis, and second, we aim to develop an overall hybrid DCA taking both hypotheses into consideration. Based on hypothesis 1, our new DCA focuses on smoothing the crisp classification task using fuzzy set theory. Based on hypothesis 2, a data set cleaning technique is used to guarantee the quality of the DCA signal data set. Results show that our proposed hybrid fuzzy maintained algorithm succeeds in obtaining results of interest. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]