Efficient Negative Selection Algorithms by Sampling and Approximate Counting

  • Johannes Textor
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7491)


Negative selection algorithms (NSAs) are immune-inspired anomaly detection schemes that are trained on normal data only: A set of consistent detectors – i.e., detectors that do not match any element of the training data – is generated by rejection sampling. Then, input elements that are matched by the generated detectors are classified as anomalous. NSAs generally suffer from exponential runtime. Here, we investigate the possibility to accelerate NSAs by sampling directly from the set of consistent detectors. We identify conditions under which this approach yields fully polynomial time randomized approximation schemes of NSAs with exponentially large detector sets. Furthermore, we prove that there exist detector types for which the approach is feasible even though the only other known method for implementing NSAs in polynomial time fails. These results provide a firm theoretical starting point for implementing efficient NSAs based on modern probabilistic techniques like Markov Chain Monte Carlo approaches.


Polynomial Time Markov Chain Monte Carlo Detector Type Sample Graph Rejection Sampling 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Johannes Textor
    • 1
  1. 1.Theoretical Biology & BioinformaticsUniversiteit UtrechtUtrechtThe Netherlands

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