A rough information extraction technique for the dendritic cell algorithm within imprecise circumstances

Abstract

The Dendritic Cell Algorithm (DCA) is an immune inspired classification algorithm based on the behavior of Dendritic Cells (DCs). The performance of DCA depends on the extracted features and their categorization to their specific signal types. These two tasks are performed during the DCA data pre-processing phase and are both based on the use of the Principal Component Analysis (PCA) information extraction technique. However, using PCA presents a limitation as it destroys the underlying semantics of the features after reduction. On the other hand, DCA uses a crisp separation between the two DCs contexts; semi-mature and mature. Thus, the aim of this paper is to develop a novel DCA version based on a two-leveled hybrid model handling the imprecision occurring within the DCA. In the top-level, our proposed algorithm applies a more adequate information extraction technique based on Rough Set Theory (RST) to build a solid data pre-processing phase. At the bottom level, our proposed algorithm applies Fuzzy Set Theory to smooth the crisp separation between the two DCs contexts. The experimental results show that our proposed algorithm succeeds in obtaining significantly improved classification accuracy. © 2014 Springer International Publishing.