Abstract
The Dendritic Cell Algorithm (DCA) is an immune algorithm based on the behavior of dendritic cells. The DCA performance relies on its data pre-processing phase which includes two sub-steps; feature selection and signal categorization. For an automatic data pre-processing task, DCA applied Rough Set Theory (RST). Nevertheless, the developed rough approach presents an information loss as data should be discretized beforehand. Thus, the aim of this paper is to develop a new DCA feature selection and signal categorization method based on Fuzzy Rough Set Theory (FRST) which allows dealing with real-valued data with no data quantization beforehand. Results show that applying FRST, instead of RST, is more convenient for the DCA data pre-processing phase yielding much better performance in terms of accuracy.
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Chelly, Z., Elouedi, Z. (2013). Supporting Fuzzy-Rough Sets in the Dendritic Cell Algorithm Data Pre-processing Phase. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42042-9_21
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DOI: https://doi.org/10.1007/978-3-642-42042-9_21
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