A New Version of the Dendritic Cell Immune Algorithm Based on the K-Nearest Neighbors

In this paper, we propose a new approach of classification based on the artificial immune Dendritic Cell Algorithm (DCA). Many researches have demonstrated the promising DCA classification results in many real world applications. Despite of that, it was shown that the DCA has a main limitation while performing its classification task. To classify a new data item, the expert knowledge is required to calculate a set of signal values. Indeed, to achieve this, the expert has to provide some specific formula capable of generating these values. Yet, the expert mandatory presence has received criticism from researchers. Therefore, in order to overcome this restriction, we have proposed a new version of the DCA combined with the K-Nearest Neighbors (KNN). KNN is used to provide a new way to calculate the signal values independently from the expert knowledge. Experimental results demonstrate the significant performance of our proposed solution in terms of classification accuracy, in comparison to several state-of-the-art classifiers, while avoiding the mandatory presence of the expert.