Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Digital Signal Processing: A Practical Approach
Digital Signal Processing: A Practical Approach
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
The Deterministic Dendritic Cell Algorithm
ICARIS '08 Proceedings of the 7th international conference on Artificial Immune Systems
Artificial Immune Systems and Kernel Methods
ICARIS '08 Proceedings of the 7th international conference on Artificial Immune Systems
The Limitations of Frequency Analysis for Dendritic Cell Population Modelling
ICARIS '08 Proceedings of the 7th international conference on Artificial Immune Systems
Geometrical insights into the dendritic cell algorithm
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Exploration of the Dendritic Cell Algorithm Using the Duration Calculus
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
Mixing linear SVMs for nonlinear classification
IEEE Transactions on Neural Networks
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
Rethinking concepts of the dendritic cell algorithm for multiple data stream analysis
ICARIS'12 Proceedings of the 11th international conference on Artificial Immune Systems
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Theoretical analyses of the Dendritic Cell Algorithm (DCA) have yielded several criticisms about its underlying structure and operation. As a result, several alterations and fixes have been suggested in the literature to correct for these findings. A contribution of this work is to investigate the effects of replacing the classification stage of the DCA (which is known to be flawed) with a traditional machine learning technique. This work goes on to question the merits of those unique properties of the DCA that are yet to be thoroughly analysed. If none of these properties can be found to have a benefit over traditional approaches, then "fixing" the DCA is arguably less efficient than simply creating a new algorithm. This work examines the dynamic filtering property of the DCA and questions the utility of this unique feature for the anomaly detection problem. It is found that this feature, while advantageous for noisy, time-ordered classification, is not as useful as a traditional static filter for processing a synthetic dataset. It is concluded that there are still unique features of the DCA left to investigate. Areas that may be of benefit to the Artificial Immune Systems community are suggested.