Summary
This work proposes an integrated rough genetic approach with modified definitions of rough set approximation for knowledge discovery from large and complex data bases. Rough set theory has been used for attribute selection while genetic algorithm has been used for finding out the right set of compact rules that covers most of the objects of the data base. The simulation results with moderately large data base have been found to be promising.
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
Mitra S., Pal S.K. and Mitra P. (2002): Data Mining in soft Computing Framework: A survey in IEEE Trans. on NN, Vol. 13, No. 1, pp.3–14.
Pawlak Z. (1991): Rough sets, Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers.
Lin T. Y. and Cercone N. (Eds.) (1997): Rough Sets and Data Mining: Analysis of Imprecise Data, Boston, Kluwer.
Modrejewski M. (1993): Feature Selection using rough sets theory in Proceedings of the European Conference on Machine Learning, pp. 213–226, Springer.
Komorowski J., Pawlak Z., Polkowski L. and Skowron A.(1999): Rough Sets: A Tutorial In Rough Fuzzy Hybridization: A New Trend in Decision Making (S. K. Pal and A. Skowron, Eds.), Springer, Singapore, pp. 3–98.
Zhong N. and Skowron A.(2001): A rough set-based knowledge discovery process in Int. J. Appl. Math. Computer Sci., Vol.11, No.3, pp. 603–619.
Preitas A. A. (2002): Data Mining and Knowledge Discovery with Evolutionary Algorithms, Springer.
Shen Q. and Chouchoulas A.(2000): A modular approach to generating fuzzy rules with reduced attributes for the monitoring of complex systems in Engineering Application of Artificial Intelligence, Vol.13, No.3, pp. 263–278.
Banerjee M.et al.(1998): Rough fuzzy MLP: Knowledge encoding and Classification in IEEE Trans. on NN, Vol 9, No. 6, pp 1203–1215.
Pal S. K., Mitra S. and Mitra P.(2003): Rough-Fuzzy MLP: Modular Evolution, Rule generation, and Evaluation in IEEE trans. on knowledge and data engineering, Vol.15, No.l, pp 14–25.
Chakraborty B.(2000): Rough-Neuro Soft Algorithm for Pattern Classification in Proceedings of KBCS’ 2000 (International conference of Knowledge based Computing Systems), pp. 554–563.
Chakraborty B.(2001): Feature Subset selection by Neuro-Rough Hybridization in Rough Sets and Current Trends in Computing, pp. 519–526, Vol. 2005 of Lecture Notes in Computer Science, Springer.
Munakata T. (1998) Fundamentals of the New Artificial Intelligence, Springer-Verlag, New York.
University of California, Irvine, Machine Learning Repository: “http://www.ics.uci.edu/mlearn/MLRepository.html”
Goldberg D. E. (1989): Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chakraborty, G., Chakraborty, B. (2005). Hybrid Rough-Genetic Algorithm for Knowledge Discovery from Large Data. In: Abraham, A., Dote, Y., Furuhashi, T., Köppen, M., Ohuchi, A., Ohsawa, Y. (eds) Soft Computing as Transdisciplinary Science and Technology. Advances in Soft Computing, vol 29. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32391-0_94
Download citation
DOI: https://doi.org/10.1007/3-540-32391-0_94
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25055-5
Online ISBN: 978-3-540-32391-4
eBook Packages: EngineeringEngineering (R0)