Computational limitations on learning from examples
Journal of the ACM (JACM)
Communications of the ACM
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
Distinguishing string selection problems
Information and Computation
Revisiting Negative Selection Algorithms
Evolutionary Computation
Theoretical advances in artificial immune systems
Theoretical Computer Science
Foundations of r-contiguous matching in negative selection for anomaly detection
Natural Computing: an international journal
Negative selection algorithms on strings with efficient training and linear-time classification
Theoretical Computer Science
A comparative study of negative selection based anomaly detection in sequence data
ICARIS'12 Proceedings of the 11th international conference on Artificial Immune Systems
Efficient negative selection algorithms by sampling and approximate counting
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
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Negative selection algorithms are immune-inspired classifiers that are trained on negative examples only. Classification is performed by generating detectors that match none of the negative examples, and these detectors are then matched against the elements to be classified. This can be a performance bottleneck: A large number of detectors may be required for acceptable sensitivity, or finding detectors that match none of the negative examples may be difficult. In this paper, we show how negative selection can be implemented without generating detectors explicitly, which for many detector types leads to polynomial time algorithms whereas the common approach to sample detectors randomly takes exponential time in the worst case. In particular, we show that negative selection on strings with generating all detectors can be efficiently simulated without detectors if, and only if, an associated decision problem can be answered efficiently, regardless the detector type. We also show how to efficiently simulate the more general case in which only a limited number of detectors is generated. For many detector types this non-exhaustive negative selection is more meaningful but it can be computationally more difficult, which we illustrate using Boolean monomials.