Part of the book series:Lecture Notes in Computer Science ((LNTCS,volume 7331))
Included in the following conference series:
3252Accesses
Abstract
This paper proposed an improved artificial immune recognition system (IAIRS) based on the average scatter matrix trace (ASMT) criterion. In essence, the artificial immune recognition system (AIRS) is an evolving algorithm. Through clonal expansion, affinity maturation, resource competition and immune memory etc, a set of new samples (memory cells) is produced. The ASMT of memory cells will be decreased and the minimized ASMT can be as the optimal criterion of AIRS. The IAIRS algorithm is demonstrated on a number of benchmark data sets effectively.
This is a preview of subscription content,log in via an institution to check access.
Access this chapter
Subscribe and save
- Get 10 units per month
- Download Article/Chapter or eBook
- 1 Unit = 1 Article or 1 Chapter
- Cancel anytime
Buy Now
- Chapter
- JPY 3498
- Price includes VAT (Japan)
- eBook
- JPY 5719
- Price includes VAT (Japan)
- Softcover Book
- JPY 7149
- Price includes VAT (Japan)
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Timmis, J., Neal, M.: A Resource limited Artificial Immune System. Knowledge Based Systems 14(3/4), 121–130 (2001)
Watkins, A., Boggess, L.: A New Classifier based on Resource Limited Artificial Immune System. In: Ebherhart, R. (ed.) Congress on Evolutionary Computation. Part of the World Congress on Computational Intelligence, Honoluu, HI, pp. 1546–1551. IEEE, Piscataway (2002)
Watkins, A., Timmis, J.: Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm. Kluwer Academic Publisher, Netherland (2003)
Zhang, L., Zhong, Y., Huang, B., Li, P.: A Resource Limited Artificial Immune System Algorithm for Supervised Classification of Multi/hyper-spectral Remote Sensing Imagery. International Journal of Remote Sensing 28(7-8), 1665–1686 (2007)
Fu, X., Zhang, S., Pang, Z.: A Resource Limited Immune Approach for Evolving Architecture and Weights of Multilayer Neural Network. In: Tan, Y., Shi, Y., Tan, K.C. (eds.) ICSI 2010, Part I. LNCS, vol. 6145, pp. 328–337. Springer, Heidelberg (2010)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. John Wiley & Sons, Beijing (2001) ISBN:0-471-05669-3
Author information
Authors and Affiliations
Department of Computer Science and Technology, Zhuhai College of Jilin University, Zhuhai, 519041, China
Xiaoyang Fu
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130012, China
Shuqing Zhang
- Xiaoyang Fu
You can also search for this author inPubMed Google Scholar
- Shuqing Zhang
You can also search for this author inPubMed Google Scholar
Editor information
Editors and Affiliations
Key Laboratory of Machine Perception (MOE), Peking University, Department of Machine Intelligence, School of Electronics Engineering and Computer Science, Peking University, 100871, Beijing, China
Ying Tan
Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China
Yuhui Shi
Shenzhen City Key Laboratory of Embedded System Design, College of Computer Science and Software Engineering, Shenzhen University, 518060, Shenzhen, China
Zhen Ji
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fu, X., Zhang, S. (2012). An Improved Artificial Immune Recognition System Based on the Average Scatter Matrix Trace Criterion. In: Tan, Y., Shi, Y., Ji, Z. (eds) Advances in Swarm Intelligence. ICSI 2012. Lecture Notes in Computer Science, vol 7331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30976-2_34
Download citation
Publisher Name:Springer, Berlin, Heidelberg
Print ISBN:978-3-642-30975-5
Online ISBN:978-3-642-30976-2
eBook Packages:Computer ScienceComputer Science (R0)
Share this paper
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative