Abstract
The Dendritic Cell Algorithm (DCA) is an immune inspired algorithm based on the behavior of dendritic cells. The performance of DCA depends on the selected features and their categorization to their specific signal types, during pre-processing. 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 DCA categorization process is based on the PCA attributes’ ranking in terms on variability. However, this categorization process could not be considered as a coherent assignment procedure. Thus, the aim of this paper is to develop a new DCA feature selection and categorization method based on Rough Set Theory (RST). In this model, the selection and the categorization processes are based on the RST CORE and REDUCT concepts. Results show that applying RST, instead of PCA, to DCA is more convenient for data pre-processing yielding much better performance in terms of accuracy.
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Chelly, Z., Elouedi, Z. (2012). RC-DCA: A New Feature Selection and Signal Categorization Technique for the Dendritic Cell Algorithm Based on Rough Set Theory. In: Coello Coello, C.A., Greensmith, J., Krasnogor, N., Liò, P., Nicosia, G., Pavone, M. (eds) Artificial Immune Systems. ICARIS 2012. Lecture Notes in Computer Science, vol 7597. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33757-4_12
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DOI: https://doi.org/10.1007/978-3-642-33757-4_12
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