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.
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
Payne T. R. and Edwards P. (1995) Learning mechanisms for information filtering agents. Department of computing science. King’s college. University of Aberdeen.
Quinlan, J. (1993) Learning logical definitions from relations. Machine Learning 5: 239–266.
Berry M. and Linoff G. (2000) Mastering Data Mining, John Wiley & Sons,.
Fayyad, U. Piatetsky-Shapiro G., Smyth, P. Uthurusamy, R. editors, (1996) Advances in Knowledge Discovery and Data Mining, AAAI/MIT Press.
Balabanovic, M., Shoham Y., and Yun Y. (1995) An adaptative agent automated web browsing. Journal of image representation and visual communication 6 (4).
Abd-Elrahman, A. M. (2000) On fuzzy Bayesian inference. International Data Analysis Conference, Innsbruck, Austria.
Mitchell, T. (1997) Machine Learning. Mc. Graw-Hill.
Yang, C. (1997) Fuzzy Bayesian Inference. Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, Orlando, FL, USA.
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.
Rennie J. and McCallum A. (1999). Using Reinforcement Learning to Spider the Web Efficiently, ICML’99.
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/.
Kosko B. (1992) Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence. Prentice Hall.
Manton K. G., Woodbury M. A. and Tolley H. D. (1994) Statistical applications using fuzzy sets. Wiley Series in Probability and Mathematical Statistics.
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).
Sheremetov, L. and Contreras M. (2001) Component Agent Platform. In Proc. of the CEEMAS’01, pp. 395–402.
Statistical Analysis System. (2001). Release 8. 1.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cortés, J.C.R., Sheremetov, L.B. (2003). Fuzzy Bayesian Classifier: a Multi-Agent System for Information Retrieval in the Web. In: Rutkowski, L., Kacprzyk, J. (eds) Neural Networks and Soft Computing. Advances in Soft Computing, vol 19. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1902-1_67
Download citation
DOI: https://doi.org/10.1007/978-3-7908-1902-1_67
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-0005-0
Online ISBN: 978-3-7908-1902-1
eBook Packages: Springer Book Archive