Immune model-based fault diagnosis
Mathematics and Computers in Simulation
How Do We Evaluate Artificial Immune Systems?
Evolutionary Computation
Multi-class iteratively refined negative selection classifier
Applied Soft Computing
A scalable artificial immune system model for dynamic unsupervised learning
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Artificial immune system programming for symbolic regression
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
A solution concept for artificial immune networks: a coevolutionary perspective
ICARIS'07 Proceedings of the 6th international conference on Artificial immune systems
ICARIS'07 Proceedings of the 6th international conference on Artificial immune systems
Review Article: Recent Advances in Artificial Immune Systems: Models and Applications
Applied Soft Computing
Principles and methods of artificial immune system vaccination of learning systems
ICARIS'11 Proceedings of the 10th international conference on Artificial immune systems
Population-based artificial immune system clustering algorithm
ICARIS'11 Proceedings of the 10th international conference on Artificial immune systems
Price trackers inspired by immune memory
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
Challenges for artificial immune systems
WIRN'05 Proceedings of the 16th Italian conference on Neural Nets
An Affinity Based Complex Artificial Immune System
International Journal of Digital Library Systems
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An investigation has been undertaken to repeat previous work on an artificial immune system for data analysis called AINE (Artificial Immune Network).The previous work was limited to testing the algorithm on relatively small data sets. The aim of this investigation is two fold,firstly to corroborate the results presented in previous work and secondly, to test the algorithm on a larger and more complex data set. A new re-implementation of AINE is then described and differences in behaviour are identified and explained. It is argued that the behaviourseen in the new implementation is more accurate than that seen in previous work and an in-depth analysis of the algorithm structure is undertaken in order to confirm theseobservations. The algorithm is also tested on new data and the results of this are presented. Comparisons are draw with other similar techniques for data mining and it is argued that AINE is an effective data-mining algorithm.