Abstract
An immune-inspired danger theory model based on dendritic cells (DCs) within the framework of fuzzy set theory is proposed in this paper. Our objective is to smooth the abrupt separation between normality (semi-mature) and abnormality (mature) using fuzzy set theory since we can neither identify a clear boundary between the two contexts nor quantify exactly what is meant by “semi-mature” or “mature”. In this model, the context of each object (DC) is described using linguistic variables. Fuzzy subsets and the corresponding membership functions describe these variables. A knowledge base, comprising rules, is built to support the fuzzy inference. The induction of the context of each object is diagnosed using a compositional rule of fuzzy inference. Experiments on real data sets show that by alleviating the crisp separation between the two contexts, our new approach which focuses on binary classification problems produces more accurate results.
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Chelly, Z., Elouedi, Z. (2010). FDCM: A Fuzzy Dendritic Cell Method. In: Hart, E., McEwan, C., Timmis, J., Hone, A. (eds) Artificial Immune Systems. ICARIS 2010. Lecture Notes in Computer Science, vol 6209. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14547-6_9
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DOI: https://doi.org/10.1007/978-3-642-14547-6_9
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