An Artificial Immune Network Model Applied to Data Clustering and Classification

Authors:
Chenggong Zhang;Zhang Yi
Affiliations:
Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, P.R. China;Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, P.R. China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Year:
2007

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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.