Abstract
A novel tree structured artificial immune network is proposed. The trunk nodes and leaf nodes represent memory antibodies and non-memory antibodies, respectively. A link is setup between two antibodies immediately after one has reproduced by another. By introducing well designed immune operators such as clonal selection, cooperation, suppression and topology updating, the network evolves from a single antibody to clusters that are well consistent with the local distribution and local density of original antigens. The framework of learning algorithm and several key steps are described. Experiments are carried out to demonstrate the learning process and classification accuracy of the proposed model.
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Zhang, C., Yi, Z. (2007). An Artificial Immune Network Model Applied to Data Clustering and Classification. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_63
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DOI: https://doi.org/10.1007/978-3-540-72393-6_63
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