An Empirical Study of Self/Non-self Discrimination in Binary Data with a Kernel Estimator

  • Thomas Stibor
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5132)


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.


Negative Selection Binary Data Kernel Estimator Probability Mass Function Shape Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 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. 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
  3. 3.
    Kullback, S., Leibler, R.A.: On information and sufficiency. The Annals of Mathematical Statistics 22(1), 79–86 (1951)zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Duda, R., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley-Interscience, Chichester (2001)zbMATHGoogle Scholar
  5. 5.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  6. 6.
    Aitchison, J., Aitken, C.G.G.: Multivariate binary discrimination by the kernel method. Biometrika 63(3), 413–420 (1976)zbMATHCrossRefMathSciNetGoogle Scholar
  7. 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. 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. 9.
    Schölkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2001)Google Scholar
  10. 10.
    Giromali, M., He, C.: Probability density estimation from optimally condensed data samples. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(10), 1253–1264 (2003)CrossRefGoogle Scholar
  11. 11.
    Fukunaga, K., Hayes, R.R.: The reduced parzen window classifier. IEEE Transaction on Pattern Analysis and Machine Intelligence 11(4), 423–425 (1989)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Thomas Stibor
    • 1
  1. 1.Department of Computer ScienceDarmstadt University of TechnologyDarmstadtGermany

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