Abstract
Negative selection algorithm (NSA) classifies a given data either as normal (self) or anomalous (non-self). To make this classification, it is trained using normal (self) samples. NSA generates detectors to cover the complementary space of self in training phase. The classification of NSAs is mainly specified by two issues, self space determination and detectors coverage. The boundary of self is ambiguous so NSAs use self samples to calculate a space close to the self space. The other issue is the detectors coverage which should maximize non-self space coverage and minimize self space coverage. This paper introduces a novel NSA and this NSA proposes k-nearest neighbor and local outlier factor to determine self space for a given self samples. Beside these, it specifies the detectors coverage using Monte Carlo Integration. The experimental evaluations show that the novel NSA generates comparable and reasonable results.
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© 2013 Springer-Verlag London
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Ataser, Z., Alpaslan, F.N. (2013). Self-Adaptive Negative Selection Using Local Outlier Factor. In: Gelenbe, E., Lent, R. (eds) Computer and Information Sciences III. Springer, London. https://doi.org/10.1007/978-1-4471-4594-3_17
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DOI: https://doi.org/10.1007/978-1-4471-4594-3_17
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