Skip to main content
SpringerLink
Log in
Book cover

Mexican International Conference on Artificial Intelligence

MICAI 2008: MICAI 2008: Advances in Artificial Intelligence pp 254–265Cite as

INDIE: An Artificial Immune Network for On-Line Density Estimation

  • Conference paper

  • 2029 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5317))

Abstract

This paper presents a new artificial immune network model that addresses the problem of non-parametric density estimation. The model combines immune ideas with the known Parzen window estimator. The model uses a general representation of antibodies, which leads to redefine the network dynamics. The model is able to perform on-line learning, that is to say, training samples are presented only once. Results from exploratory experiments are presented in order to give insights on the reliability of the estimations of the proposed model.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Alpaydin, E.: Introduction to Machine Learning. The MIT Press, Cambridge (2004)

    MATH  Google Scholar 

  2. Girolami, M., He, C.: Probability density estimation from optimally condensed data samples. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 1253–1264 (2003)

    Article  Google Scholar 

  3. Stibor, T., Timmis, J.: An investigation into the compression quality of aiNET. In: Foundations of Computational Intelligence. IEEE Symposium on Computational Intelligence, HI, USA, pp. 495–502 (2007)

    Google Scholar 

  4. Alonso, O., González, F.A., Niño, F., Galeano-Huertas, J.C.: A solution concept for artificial immune networks: A coevolutionary perspective. In: de Castro, L.N., Von Zuben, F.J., Knidel, H. (eds.) ICARIS 2007. LNCS, vol. 4628, pp. 35–46. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  5. Deng, Z., Chung, F.L., Wang, S.: FRSDE: Fast reduced set density estimator using minimal enclosing ball approximation. Pattern Recognition 41, 1363–1372 (2008)

    Article  MATH  Google Scholar 

  6. de Castro, L.N., Zuben, F.J.V.: aiNet: An Artificial Immune Network for Data Analysis. In: Abbas, H.A., Sarker, R.A., Newton, C.S. (eds.) Data Mining: A Heuristic Approach, pp. 231–259. Idea Group Publishing, USA (2001)

    Google Scholar 

  7. Nasraoui, O., Cardona, C., Rojas, C., González, F.: TECNO-STREAMS: Tracking evolving clusters in noisy data streams with a scalable immune system learning model. In: Third IEEE International Conference on Data Mining, Melbourne, FL. IEEE, Los Alamitos (2003)

    Google Scholar 

  8. Nasraoui, O., González, F., Dasgupta, D.: The Fuzzy Artificial Immune System: Motivations, Basic Concepts and Application to Clustering and Web Profiling. In: IEEE International Conference on Fuzzy Systems, Hawaii, HI, pp. 711–716. IEEE, Los Alamitos (2002)

    Google Scholar 

  9. Neal, M.: An Artificial Immune System for Continuous Analysis of Time-Varying Data. In: Timmis, J., Bentley, P.J. (eds.) Proceedings of the 1st International Conference on Artificial Immune Systems (ICARIS), vol. 1, pp. 76–85. University of Kent at Canterbury, University of Kent at Canterbury Printing Unit (2002)

    Google Scholar 

  10. Neal, M.: Meta-Stable Memory in an Artificial Immune Network. In: Timmis, J., Bentley, P., Hart, E. (eds.) ICARIS 2003. LNCS, vol. 2787, pp. 168–180. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  11. Timmis, J., Neal, M.: A Resource Limited Artificial Immune System for Data Analysis. Knowledge-Based Systems 14, 121–130 (2001)

    Article  Google Scholar 

  12. Timmis, J., Neal, M., Hunt, J.: An Artificial Immune System for Data Analysis. BioSystems 55, 143–150 (2000)

    Article  Google Scholar 

  13. de Castro, L.N., Timmis, J.: An Artificial Immune Network for Multimodal Optimisation. In: Congress on Evolutionary Computation. Part of the 2002 IEEE World Congress on Computational Intelligence, Honolulu, Hawaii, USA, pp. 699–704. IEEE, Los Alamitos (2002)

    Google Scholar 

  14. Ishiguro, A., Ichikawa, S., Uchikawa, Y.: A Gait Acquisition of Six-Legged Robot Using Immune Networks. In: Proceedings of International Conference on Intelligent Robotics and Systems (IROS 1994), Munich, Germany, vol. 2, pp. 1034–1041 (1994)

    Google Scholar 

  15. Luh, G.C., Liu, W.W.: Reactive Immune Network Based Mobile Robot Navigation. In: Nicosia, G., Cutello, V., Bentley, P.J., Timmis, J. (eds.) Proceeding of the Third Conference ICARIS, pp. 119–132. Springer, Heidelberg (2004)

    Google Scholar 

  16. Michelan, R., Zuben, F.J.V.: Decentralized Control System for Autonomous Navigation Based on an Evolved Artificial Immune Network. In: Proceedings of the IEEE Congress on Evolutionary Computation, Honolulu, HI, vol. 2, pp. 1021–1026. IEEE, Los Alamitos (2002)

    Google Scholar 

  17. Vargas, P.A., de Castro, L.N., Michelan, R., Zuben, F.J.V.: An Immune Learning Classifier System for Autonomous Navigation. In: Timmis, J., Bentley, P., Hart, E. (eds.) Proceedings of the Second International Conference ICARIS, Edinburg, UK, pp. 69–80. Springer, Heidelberg (2003)

    Google Scholar 

  18. Galeano, J.C., Veloza-Suan, A., González, F.A.: A comparative analysis of artificial immune network models. In: Beyer, H. (ed.) Proceedings of the Genetic and Evolutionary Computation Conference, pp. 361–368. ACM Press, New York (2005)

    Google Scholar 

  19. Perelson, A.S., Weisbuch, G.: Immunology for Physicists. Reviews of Modern Physics 69, 1219–1267 (1997)

    Article  Google Scholar 

  20. de Castro, L.N., Timmis, J.: Artificial Immune Systems: A New Computational Intelligence Approach. Springer, Heidelberg (2002)

    MATH  Google Scholar 

  21. Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)

    Book  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Intelligent Systems Research Lab., National University of Colombia, Colombia

    Juan Carlos Galeano-Huertas & Fabio A. González

Authors
  1. Juan Carlos Galeano-Huertas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fabio A. González
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Center for Computing Research, National Polytechnic Institute, 07738, Mexico City, México

    Alexander Gelbukh

  2. Ciencias Computacionales, Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE), Luis Enrique Erro #1 , Sta. María Tonantzintla, 72840, Puebla, México

    Eduardo F. Morales

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Galeano-Huertas, J.C., González, F.A. (2008). INDIE: An Artificial Immune Network for On-Line Density Estimation. In: Gelbukh, A., Morales, E.F. (eds) MICAI 2008: Advances in Artificial Intelligence. MICAI 2008. Lecture Notes in Computer Science(), vol 5317. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88636-5_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-88636-5_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-88635-8

  • Online ISBN: 978-3-540-88636-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation