Further exploration of the fuzzy dendritic cell method
ICARIS'11 Proceedings of the 10th international conference on Artificial immune systems
Introducing dendritic cells as a novel immune-inspired algorithm for anomaly detection
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
COID-FDCM: the fuzzy maintained dendritic cell classification method
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
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The Dendritic Cell Algorithm (DCA) is an immune-inspired classification algorithm based on the behavior of dendritic cells. The DCA performance depends on its data pre-processing phase including feature selection and their categorization to specific signal types. For feature selection, DCA applies the principal component analysis (PCA). Nevertheless, PCA does not guarantee that the selected first principal components will be the most adequate for classification. Furthermore, the categorization of features to their specific signal types is based on the PCA attributes' ranking in terms on variability which does not make "sense". Thus, the aim of this paper is to develop a new DCA data pre-processing method based on Rough Set Theory (RST). In this newly-proposed hybrid DCA model, the selection and the categorization of attributes are based on the RST CORE and REDUCT concepts. Results show that using RST instead of PCA for the DCA data pre-processing phase yields much better performance in terms of classification accuracy.