Zeineb Chelly
ABSTRACT
The aim of this paper is to develop a new data pre-processing method for the dendritic cell algorithm (DCA) based on Fuzzy Rough Set Theory (FRST). In this new fuzzy-rough model, the data pre-processing phase is based on the fuzzy positive region and the fuzzy dependency degree concepts. Results show that applying FRST is more convenient for the DCA data pre-processing phase yielding much better performance in terms of accuracy.
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Digital Library
- Z. Chelly and Z. Elouedi. Rc-dca: A new feature selection and signal categorization technique for the dendritic cell algorithm based on rough set theory. In ICARIS, pages 152--165, 2012. Google ScholarDigital Library
- Z. Pawlak. Rough sets. International Journal of Computer and Information Science, 11:341--356, 1982.Google ScholarCross Ref
- D. Dubois and H. Prade. Putting rough sets and fuzzy sets together. Kluwer Academic Publishers, Dordrecht, 1992.Google ScholarCross Ref
- A. Asuncion and D.J. Newman. UCI machine learning repository, http://mlearn.ics.uci.edu/mlrepository.html, 2007.Google Scholar
- J. Greensmith and U. Aickelin. The deterministic dendritic cell algorithm. In ICARIS, pages 291--302, 2008. Google ScholarDigital Library
Index Terms
- A new data pre-processing approach for the dendritic cellalgorithm based on fuzzy rough set theory
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