IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Discriminating self from non-self with finite mixtures of multivariate Bernoulli distributions
Proceedings of the 10th annual conference on Genetic and evolutionary computation
T Cell Receptor Signalling Inspired Kernel Density Estimation and Anomaly Detection
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
Review Article: Recent Advances in Artificial Immune Systems: Models and Applications
Applied Soft Computing
A comparative study of negative selection based anomaly detection in sequence data
ICARIS'12 Proceedings of the 11th international conference on Artificial Immune Systems
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Affinity functions play a major role within the artificial immune system (AIS) framework and crucially bias the performance of AIS algorithms. In the problem domain of self/non-self discrimination by means of negative selection, affinity functions such as the Hamming distance or the r-contiguous distance are frequently applied to measure distances in binary data. In recent years however, several limitations and problems with these distance measurements in negative selection have been identified. We propose to measure distances in binary data by means of probabilities which are modeled with a kernel estimator. Such a probabilistic model is preeminently applicable for the self/non-self discrimination problem. We underpin our proposal with an empirical study on artificially generated and real-world datasets.