Self-adaptive negative selection using local outlier factor

Date

2012-09-05

Author

Ataser, Zafer
Alpaslan, Ferda Nur

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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.

Subject Keywords

Nearest neighbor search, Statistics

URI

https://hdl.handle.net/11511/88080

DOI

https://doi.org/10.1007/978-1-4471-4594-3_17

Conference Name

27th International Symposium on Computer and Information Sciences, ISCIS 2012, (3 - 05 Eylül 2012)

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

Z. Ataser and F. N. Alpaslan, “Self-adaptive negative selection using local outlier factor,” presented at the 27th International Symposium on Computer and Information Sciences, ISCIS 2012, (3 - 05 Eylül 2012), 2012, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/88080.