Abstract
The Dendritic Cell Algorithm (DCA) is an immune-inspired classification algorithm based on the behavior of natural dendritic cells (DC). This paper proposes a novel version of the DCA based on a two-level hybrid fuzzy-rough model. In the top-level, the proposed algorithm, named RST-MFDCM, applies rough set theory to build a solid data pre-processing phase. In the second level, RST-MFDCM applies fuzzy set theory to smooth the crisp separation between the DC's semi-mature and mature contexts. The experimental results show that RST-MFDCM succeeds in obtaining significantly improved classification accuracy. © 2013 Springer-Verlag Berlin Heidelberg.
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CITATION STYLE
Chelly, Z., & Elouedi, Z. (2013). A new hybrid fuzzy-rough dendritic cell immune classifier. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7928 LNCS, pp. 514–521). https://doi.org/10.1007/978-3-642-38703-6_60
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