Fuzzy Modeling for Control
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
The Deterministic Dendritic Cell Algorithm
ICARIS '08 Proceedings of the 7th international conference on Artificial Immune Systems
Introducing dendritic cells as a novel immune-inspired algorithm for anomaly detection
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Effect of rule weights in fuzzy rule-based classification systems
IEEE Transactions on Fuzzy Systems
COID-FDCM: the fuzzy maintained dendritic cell classification method
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
ICARIS'12 Proceedings of the 11th international conference on Artificial Immune Systems
RST-DCA: a dendritic cell algorithm based on rough set theory
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
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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.