Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial immune systems---today and tomorrow
Natural Computing: an international journal
Theoretical advances in artificial immune systems
Theoretical Computer Science
Information fusion for anomaly detection with the dendritic cell algorithm
Information Fusion
On the use of hyperspheres in artificial immune systems as antibody recognition regions
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
Not all balls are round: an investigation of alternative recognition-region shapes
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Revisiting the Foundations of Artificial Immune Systems for Data Mining
IEEE Transactions on Evolutionary Computation
Quiet in class: classification, noise and the dendritic cell algorithm
ICARIS'11 Proceedings of the 10th international conference on Artificial immune systems
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In this paper, we focus on the potential for applying Kernel Methods into Artificial Immune Systems. This is based on the fact that the commonly employed "affinity functions" can usually be replaced by kernel functions, leading to algorithms operating in the feature space. A discussion of this applicability in negative/positive selection algorithms, the dendritic cell algorithm and immune network algorithms is conducted. As a practical application, we modify the aiNet (Artificial Immune Network) algorithm to use a kernel function, and analyze its compression quality using synthetic datasets. It is concluded that the use of properly adjusted kernel functions can improve the compression quality of the algorithm. Furthermore, we briefly discuss some of the future implications of using kernel functions in immune-inspired algorithms.