Advertisement

Fuzzy Bayesian Classifier: a Multi-Agent System for Information Retrieval in the Web

  • José C. Romero Cortés
  • Leonid B. Sheremetov
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 19)

Summary

A fuzzy Bayesian approach helping an Internet user to filter Web pages is discussed. In the proposed approach, one page can be classified as having the continuous quality of being interesting, this means that a certain grade of membership can be associated with each page relative to a category of selection. Filtering is based on the evidences of the content of the page title, abstract or complete document. An example comparing crisp and fuzzy classifiers implemented as a part of multi-agent system to support information filtering and retrieval in the Web is discussed illustrating the proposed approach.

Keywords

Posteriori Probability Page Title Agent Platform Fuzzy Classifier Search Agent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Payne T. R. and Edwards P. (1995) Learning mechanisms for information filtering agents. Department of computing science. King’s college. University of Aberdeen.Google Scholar
  2. 2.
    Quinlan, J. (1993) Learning logical definitions from relations. Machine Learning 5: 239–266.Google Scholar
  3. 3.
    Berry M. and Linoff G. (2000) Mastering Data Mining, John Wiley & Sons,.Google Scholar
  4. 4.
    Fayyad, U. Piatetsky-Shapiro G., Smyth, P. Uthurusamy, R. editors, (1996) Advances in Knowledge Discovery and Data Mining, AAAI/MIT Press.Google Scholar
  5. 5.
    Balabanovic, M., Shoham Y., and Yun Y. (1995) An adaptative agent automated web browsing. Journal of image representation and visual communication 6 (4).Google Scholar
  6. 6.
    Abd-Elrahman, A. M. (2000) On fuzzy Bayesian inference. International Data Analysis Conference, Innsbruck, Austria.Google Scholar
  7. 7.
    Mitchell, T. (1997) Machine Learning. Mc. Graw-Hill.Google Scholar
  8. 8.
    Yang, C. (1997) Fuzzy Bayesian Inference. Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, Orlando, FL, USA.Google Scholar
  9. 9.
    Edwards P., Bayer D., Green C.L., and Payne T. (1996) Experience with learning agents with manage internet-based information. University of Aberdeen, Scotland, AB9 2UE.Google Scholar
  10. 10.
    Rennie J. and McCallum A. (1999). Using Reinforcement Learning to Spider the Web Efficiently, ICML’99.Google Scholar
  11. 11.
    Muller M. E. (1999) An Intelligent Multi-Agent Architecture for Information Retrieval from the Internet. Technical report, Institute for Semantic Information Processing, Univ. Osnabrück, available from http://www.aye-aye.de/martin/.Google Scholar
  12. 12.
    Kosko B. (1992) Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence. Prentice Hall.Google Scholar
  13. 13.
    Manton K. G., Woodbury M. A. and Tolley H. D. (1994) Statistical applications using fuzzy sets. Wiley Series in Probability and Mathematical Statistics.Google Scholar
  14. 14.
    Romero J., C., (2000) Bayesian fuzzy agent to filter and recover information in Web pages. In Proc. of the International Congress in Computing. pp. 472–480 (in Spanish).Google Scholar
  15. 15.
    Sheremetov, L. and Contreras M. (2001) Component Agent Platform. In Proc. of the CEEMAS’01, pp. 395–402.Google Scholar
  16. 16.
    Statistical Analysis System. (2001). Release 8. 1.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • José C. Romero Cortés
    • 1
    • 2
  • Leonid B. Sheremetov
    • 1
    • 3
  1. 1.Center for Computing Research of the National Technical University, (CICIPN)Mexico, D.F.
  2. 2.Metropolitan Autonomous UniversityAcapotzalcoMexico, D.F.
  3. 3.Mexican Oil InstituteMexico DF.

Personalised recommendations