Abstract
The Dendritic Cell Algorithm (DCA) is an immune inspired classification algorithm based on the behavior of natural dendritic cells. The DCA performance relies on its data pre-processing phase based on the Principal Component analysis (PCA) statistical method. However, using PCA presents a limitation as it destroys the underlying semantics of the features after reduction. One possible solution to overcome this limitation was the application of Rough Set Theory (RST) in the DCA data pre-processing phase; but still the developed rough DCA approach presents an information loss as data should be discretized beforehand. Thus, the aim of this paper is to develop a new DCA data pre-processing method based on Fuzzy Rough Set Theory (FRST) which allows dealing with real-valued data with no data quantization beforehand. In this new fuzzy-rough model, the DCA data pre-processing phase is based on the FRST concepts; mainly the fuzzy lower and fuzzy upper approximations. Results show that applying FRST, instead of PCA and RST, 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. (2013). A Fuzzy-Rough Data Pre-processing Approach for the Dendritic Cell Classifier. In: van der Gaag, L.C. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2013. Lecture Notes in Computer Science(), vol 7958. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39091-3_10
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DOI: https://doi.org/10.1007/978-3-642-39091-3_10
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