An Empirical Study of Self/Non-self Discrimination in Binary Data with a Kernel Estimator
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.
KeywordsNegative Selection Binary Data Kernel Estimator Probability Mass Function Shape Space
Unable to display preview. Download preview PDF.
- 1.Forrest, S., Perelson, A.S., Allen, L., Cherukuri, R.: Self-nonself discrimination in a computer. In: Proceedings of the Symposium on Research in Security and Privacy, pp. 202–212. IEEE Computer Society Press, Los Alamitos (1994)Google Scholar
- 2.Stibor, T.: On the Appropriateness of Negative Selection for Anomaly Detection and Network Intrusion Detection. PhD thesis, Darmstadt University of Technology (2006)Google Scholar
- 7.Stibor, T.: Discriminating self from non-self with finite mixtures of multivariate bernoulli distributions. In: Proceedings of Genetic and Evolutionary Computation Conference – GECCO. ACM Press, New York (to appear, 2008)Google Scholar
- 8.González, F., Dasgupta, D., Gómez, J.: The effect of binary matching rules in negative selection. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 195–206. Springer, Heidelberg (2003)CrossRefGoogle Scholar
- 9.Schölkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2001)Google Scholar