Review Article: Recent Advances in Artificial Immune Systems: Models and Applications
Applied Soft Computing
Artificial Immune Systems: Models, Applications, and challenges
Proceedings of the 27th Annual ACM Symposium on Applied Computing
An artificial immune system approach to associative classification
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part I
AC-CS: an immune-inspired associative classification algorithm
ICARIS'12 Proceedings of the 11th international conference on Artificial Immune Systems
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For time-variation and nonlinearity of vehicle image classifier design in complicated environment, a novel classifier based on Immune Clone Concentration Clustering Algorithm (ICCCA) was proposed, inspired by the clone selection theory in the natural immune system. It overcame the defects of traditional classification algorithms as multi-restraint and trap of local optimum. It also avoided the high complexity and regulating dissimilation caused by the cross and variation rules of exiting immune clone clustering algorithms. Directing towards two-class image pattern recognition problem, we defined the antibody and antigen of vehicle image, established class divisibility standard, and designed the affine mathematical model based on the antibody and antigen's Euclidean distance and their concentration. We proposed a method to create immune clone memory cell by the antibodies concentration as long as concentration regulating. The algorithm can guarantee the antibody diversity, and obtain the global optimal solution quickly and precisely. The experimental results verified that the classification accuracy of the algorithm is superior to other classification algorithm.