Titlebook: Artificial Immune Systems; 6th International Co Leandro Nunes Castro,Fernando José Zuben,Helder Kn Conference proceedings 2007 Springer-Ver

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he proposed framework approximately achieved 100% detection rate with negligible false positive rate. One can conclude from the ROC (Receiver Operating Characteristics) plots of our AIS that its performance approaches ‘perfect classification point’ at a suitable matching threshold value.

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Registrier- und Auswertetechnik,trol of various aspects of the immune system, and includes a mechanism for signalling between cells. Preliminary results of the application of the proposed model to the DAMADICS fault detection dataset are presented, indicating that the proposed approach can attain good results when properly tuned.

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Klaus W. Lange,Georges Rentizelasositive Selection and the Clonal Selection Algorithms adopted from Human Immune System. Finally we compare our proposed models with a few existing statistical and mathematical sickness prediction methods.

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The Influence of Diversity in an Immune–Based Algorithm to Train MLP Networksal and ensembles of classifiers. Two different classes of algorithms to train MLP are tested: bio-inspired, and gradient-based. Comparisons among all the training methods are presented in terms of classification accuracy and diversity of the solutions found.

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Bankruptcy Prediction Using Artificial Immune Systemsositive Selection and the Clonal Selection Algorithms adopted from Human Immune System. Finally we compare our proposed models with a few existing statistical and mathematical sickness prediction methods.