Is negative selection appropriate for anomaly detection?

Thomas Stibor*, Philipp Mohr, Jonathan Timmis

*Awdur cyfatebol y gwaith hwn

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion CynhadleddTrafodion Cynhadledd (Nid-Cyfnodolyn fathau)

144 Dyfyniadau(SciVal)

Crynodeb

Negative selection algorithms for hamming and real-valued shape-spaces are reviewed. Problems are identified with the use of these shape-spaces, and the negative selection algorithm in general, when applied to anomaly detection. A straightforward self detector classification principle is proposed and its classification performance is compared to a real-valued negative selection algorithm and to a one-class support vector machine. Earlier work suggests that real-value negative selection requires a single class to learn from. The investigations presented in this paper reveal, however, that when applied to anomaly detection, the real-valued negative selection and self detector classification techniques require positive and negative examples to achieve a high classification accuracy. Whereas, one-class SVMs only require examples from a single class.

Iaith wreiddiolSaesneg
TeitlGECCO 2005 - Genetic and Evolutionary Computation Conference
GolygyddionH.G. Beyer, U.M. O'Reilly, D. Arnold, W. Banzhaf, C. Blum, E.W. Bonabeau, E. Cantu-Paz, D. Dasgupta, K. Deb, al et al
Tudalennau321-328
Nifer y tudalennau8
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 2005
DigwyddiadGECCO 2005 - Genetic and Evolutionary Computation Conference - Washington, D.C., Unol Daleithiau America
Hyd: 25 Meh 200529 Meh 2005

Cyfres gyhoeddiadau

EnwGECCO 2005 - Genetic and Evolutionary Computation Conference

Cynhadledd

CynhadleddGECCO 2005 - Genetic and Evolutionary Computation Conference
Gwlad/TiriogaethUnol Daleithiau America
DinasWashington, D.C.
Cyfnod25 Meh 200529 Meh 2005

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