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As one of the newest members in the field of artificial immune systems (AIS),the Dendritic Cell Algorithm (DCA) is based on behavioural models of naturaldendritic cells (DCs). Unlike other AIS, the DCA does not rely on trainingdata, instead domain or expert knowledge is required to predetermine themapping between input signals from a particular instance to the threecategories used by the DCA. This data preprocessing phase has received thecriticism of having manually over-?tted the data to the algorithm, which isundesirable. Therefore, in this paper we have attempted to ascertain if it ispossible to use principal component analysis (PCA) techniques to automaticallycategorise input data while still generating useful and accurate classicationresults. The integrated system is tested with a biometrics dataset for thestress recognition of automobile drivers. The experimental results have shownthe application of PCA to the DCA for the purpose of automated datapreprocessing is successful.

PCA 4 DCA: The Application Of Principal Component Analysis To The Dendritic Cell Algorithm