Abstract
A new immune-inspired model of the fuzzy dendritic cell method is proposed in this paper. Our model is based on the function of dendritic cells within the framework of fuzzy set theory and fuzzy c-means clustering. Our purpose is to use fuzzy set theory to smooth the crisp separation between DCs’ contexts (semi-mature and mature) since we can neither identify a clear boundary between them nor quantify exactly what is meant by “semi-mature” or “mature”. In addition, we aim at generating automatically the extents and midpoints of the membership functions which describe the variables of the model using fuzzy c-means clustering. Hence, we can avoid negative influence on the results when an ordinary user introduces such parameters. Simulations on binary classification databases show that by alleviating the crisp separation between the two contexts and generating automatically the extents of the membership functions, our method produces more accurate results.
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Chelly, Z., Elouedi, Z. (2011). Further Exploration of the Fuzzy Dendritic Cell Method. In: Liò, P., Nicosia, G., Stibor, T. (eds) Artificial Immune Systems. ICARIS 2011. Lecture Notes in Computer Science, vol 6825. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22371-6_36
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DOI: https://doi.org/10.1007/978-3-642-22371-6_36
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