Abstract
In this paper, we propose a new approach of data pre- processing based on rough set theory for the Dendritic Cell Algorithm (DCA). Our hybrid immune inspired model, denoted QR-DCA, is based on the functioning of dendritic cells within the framework of rough set theory and more precisely, on the QuickReduct algorithm. As the DCA data pre-processing phase is divided into two sub-steps, feature selection and signal categorization, our QR-DCA model selects the right features for the DCA classification task and categorizes each one of them to its specific signal category. This is achieved while preserving the same DCA main characteristic which is its lightweight in terms of running time. Results show that our new approach generates good classification results. We will also compare our QR-DCA to other rough DCA models to show that our new approach outperforms them in terms of classification accuracy while keeping the worthy characteristics expressed by the DCA.
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Chelly, Z., Elouedi, Z. (2013). QR-DCA: A New Rough Data Pre-processing Approach for the Dendritic Cell Algorithm. In: Tomassini, M., Antonioni, A., Daolio, F., Buesser, P. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2013. Lecture Notes in Computer Science, vol 7824. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37213-1_15
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DOI: https://doi.org/10.1007/978-3-642-37213-1_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37212-4
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