Abstract
Knowledge discovery approaches based on rough sets have successful application in machine learning and data mining. As these approaches are good at dealing with discrete values, a discretizer is required when the approaches are applied to continuous attributes. In this paper, a novel adaptive discretizer based on a statistical distribution index is proposed to preprocess continuous valued attributes in an instance information system, so that the knowledge discovery approaches based on rough sets can reach a high decision accuracy. The experimental results on benchmark data sets show that the proposed discretizer is able to improve the decision accuracy.
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
Lin, T.Y., Cercone, N. (eds.): Rough Set and Data Mining. Kluwer Academic Publishers, Dordrecht (1997)
Polkowski, L., Tsumoto, S., Lin, T.Y.: Rough Set Methods and Applications: New Developments in Knowledge Discovery in Information Systems. Physica-Verlag, Springer (2000)
Hu, X., Cecone, N., Ziarko, W.: Generation of multiple knowledge from databases based on rough set theory 1, 109–121 (1997)
Wu, Q.X., Bell, D.A., McGinnity, T.M.: Multi-knowledge for Decision Making. International Journal of Knowledge and Information Systems 2, 246–266 (2005)
Dougherty, J., Kohavi, R., Sahami, M.: Supervised and Unsupervised Discretization of Continuous Features. In: Proceedings of International Conference on Machine Learning, pp. 194–202 (1995)
Wu, X.: A Bayesian Discretizer for Real-Valued Attributes. The Computer J. 8, 688–691 (1996)
Kurgan, L.A., Cios, K.J.: CAIM Discretization Algorithm. IEEE Transaction on Knowledge and Data Engineering 2, 145–153 (2004)
Pawlak, Z.: Rough sets: theoretical aspects data analysis. Kluwer Academic Publishers, Dordrecht (1991)
Blake, C.L., Merz, C.J.: UCI Repository of Machine Learning DatabasesUC Irvine, Dept. Information and Computer Science (Download in 2003), http://www.ics.uci.edu/~mlearn/MLRepository.html
Quinlan, J.R.: Induction of Decision Trees. Machine Learning 1, 81–106 (1986)
Mitchell, M.T.: Machine Learning. McGraw Hill Co-published by MIT Press, Cambridge (1997)
Wu, Q.X., Bell, D.A.: Multi-Knowledge Extraction and Application. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds.) RSFDGrC 2003. LNCS (LNAI), vol. 2639, pp. 274–279. Springer, Heidelberg (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wu, Q., Cai, J., Prasad, G., McGinnity, T.M., Bell, D., Guan, J. (2006). A Novel Discretizer for Knowledge Discovery Approaches Based on Rough Sets. In: Wang, GY., Peters, J.F., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2006. Lecture Notes in Computer Science(), vol 4062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11795131_35
Download citation
DOI: https://doi.org/10.1007/11795131_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36297-5
Online ISBN: 978-3-540-36299-9
eBook Packages: Computer ScienceComputer Science (R0)