Abstract
Despite numerous theoretical investigations on Artificial negative selection (ANS), there are still no useful scenarios in which this paradigm would outperform mainstream machine learning, statistical classification methods or other bio-inspired classification approaches. The aim of this paper is to identify main characteristics and requirements of a useful ANS scenario. Our investigations on this question led us to the need to extend the original ANS model proposed by Forrest et al. in [4]. The motivation of our work relies on the observation that biological mechanisms are not isolated mechanisms with a broad application range. They are only suitable for highly specific tasks and they might only be efficient in interaction with the rest of the biological environment.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Balthrop, J., Esponda, F., Forrest, S., Glickman, M.: Coverage and generalization in an artificial immune system. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 3–10 (2002)
D’haeseleer, P., Forrest, S., Helman, P.: An immunological approach to change detection: algorithms, analysis, and implications. In: Proceedings of the 1996 IEEE Symposium on Computer Security and Privacy (1996)
Elberfeld, M., Textor, J.: Efficient Algorithms for String-Based Negative Selection. In: Andrews, P.S., Timmis, J., Owens, N.D.L., Aickelin, U., Hart, E., Hone, A., Tyrrell, A.M. (eds.) ICARIS 2009. LNCS, vol. 5666, pp. 109–121. Springer, Heidelberg (2009)
Forrest, S., Perelson, A.S., Allen, L., Cherukuri, R.: Self-nonself discrimination in a computer. In: Proceedings of the 1994 IEEE Symposium on Research in Security and Privacy, pp. 202–212 (1994)
Gonzalez, F.A.: A study of artificial immune systems applied to anomaly detection. PhD thesis (2003)
Ji, Z., Dasgupta, D.: Real-Valued Negative Selection Algorithm with Variable-Sized Detectors. In: Deb, K., Tari, Z. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 287–298. Springer, Heidelberg (2004)
Košmrlj, A., Chakraborty, A., Kardar, M., Shakhnovich, E.I.: Thymic selection of t-cell receptors as an extreme value problem (2009)
Percus, Percus: String matching for the novice. AMM 101 (1994)
Stibor, T., Mohr, P.H., Timmis, J., Eckert, C.: Is negative selection appropriate for anomaly detection? In: GECCO, pp. 321–328 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Nebesov, Y. (2012). Artificial Negative Selection: Searching for an Appropriate Application Scenario. In: Hart, E., Timmis, J., Mitchell, P., Nakamo, T., Dabiri, F. (eds) Bio-Inspired Models of Networks, Information, and Computing Systems. BIONETICS 2011. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32711-7_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-32711-7_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32710-0
Online ISBN: 978-3-642-32711-7
eBook Packages: Computer ScienceComputer Science (R0)