Negative selection algorithms on strings with efficient training and linear-time classification

@article{Elberfeld2011NegativeSA,
  title={Negative selection algorithms on strings with efficient training and linear-time classification},
  author={Michael Elberfeld and Johannes Textor},
  journal={Theor. Comput. Sci.},
  year={2011},
  volume={412},
  pages={534-542}
}
A string-based negative selection algorithm is an immune-inspired classifier that infers a partitioning of a string space Σ into “normal” and “anomalous” partitions from a training set S containing only samples from the “normal” partition. The algorithm generates a set of patterns, called “detectors”, to cover regions of the string space containing none of the training samples. Strings that match at least one of these detectors are then classified as “anomalous”. A major problem with existing… CONTINUE READING

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