Improving the Dendritic Cell Algorithm Performance Using Fuzzy-Rough Set Theory as a Pattern Discovery Technique
As an immune inspired algorithm, the Dendritic Cell Algorithm (DCA) is based on the behavior of biological dendritic cells. The performance of DCA relies on its data pre-processing phase; including feature selection and signal categorization. For an automatic data pre-processing task, DCA applied Rough Set Theory (RST). However, applying RST as a pre-processor presents an information loss as data should be discretized beforehand. Therefore, the aim of this paper is to propose a new DCA data pre-processing phase based on a more efficient pattern discovery technique which is Fuzzy Rough Set Theory (FRST). FRST allows dealing with real-valued data with no data quantization beforehand. In our newly proposed fuzzy-rough model, the data pre-processing phase is based on FRST and more precisely on the use of the fuzzy positive region and the fuzzy dependency degree concepts. The experimental results show that our proposed algorithm succeeds in obtaining significantly improved classification accuracy.
KeywordsArtificial Immune Systems Dendritic Cells Fuzzy-Rough Set Theory Pattern Discovery
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