Abstract
This paper deals with the simplification of classification rules for data mining using rough sets theory combined with neural networks. In the attribute reduction process, the proposed approach generates minimal reduct and minimum number of rules with high accuracy. Experimental results with sample data sets in UCI repository show that this method gives a good performance in getting concise and accurate rules.
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
Yasdi, R.: Combining Rough Sets Learning and Neural Learning Method to Deal with Uncertain and Imprecise Information. Neurocomputation 7, 61–84 (1995)
Mitra, P., Pal, S.K.: Staging of Cervical Cancer with Soft Computing. IEEE Trans. on Biomedical Engineering 47(7), 934–940 (2000)
Ahn, B.S., Cho, S.S., Kim, C.Y.: The Integrated Methodology of Rough Set Theory and Artificial Neural Network for Business Failure Prediction. Expert Systems with Applications 18, 65–74 (2000)
Pal, S.K., Mitra, S., Mitra, P.: Rough-Fuzzy MLP: Modular Evolution, Rule Generation, and Evaluation. IEEE Trans. on Knowledge and Data Engineering 15(1), 14–25 (2003)
Pawlak, Z., Grzymala-Busse, J., Slowinski, R., Ziarko, W.: Rough Sets. Communications of the ACM 38(11), 88–95 (1995)
Hu, X., Cercone, N., Ziarko, W.: Generation of Multiple Knowledge from Databases Based on Rough Sets Theory. Rough Sets and Data Mining, pp. 109–121. Kluwer, Dordrecht (1997)
Kusiak, A.: Rough Set Theory: A Data Mining Tool for Semiconductor Manufacturing. IEEE Trans. on Electronics Packaging Manufacturing 24(1), 44–50 (2001)
Fayyad, U.M., Irani, K.B.: Multi-interval Discretization of Continuous-valued Attributes for Classification Learning. In: Proc. of the 13th International Joint Conference on Artificial Intelligence, pp. 1022–1027. Morgan Kaufmann, San Francisco (1993)
Pan, D., Zheng, Q.-L., Zeng, A., Hu, J.-S.: A Novel Self-Optimizing Approach for Knowledge Acquisition. IEEE Trans. on Systems, Man. and Cybernetics-Part A: Systems and Humans 32, 505–514 (2002)
Starzyk, J., Nelson, D., Sturtz, K.: A Mathematical Foundation for Improved Reduct Generation in Information Systems. Knowledge and Information Systems 2, 131–146 (2000)
Chen, X., Zhu, S., Ji, Y.: Entropy Based Uncertainty Measures for Classfication Rules with Inconsistency Tolerance. In: Proc. of the IEEE International Conference on Systems, Man and Cybernetics, pp. 2816–2821 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Xu, Y., Chaudhari, N.S. (2004). Rule Extraction Framework Using Rough Sets and Neural Networks. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_155
Download citation
DOI: https://doi.org/10.1007/978-3-540-30499-9_155
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23931-4
Online ISBN: 978-3-540-30499-9
eBook Packages: Springer Book Archive