Abstract
In order to overcome the difficulties in attribute reduction with large quantity of condition attributes, genetic algorithm was employed to obtain the minimal reduction of decision tables under existed conditions by combining its outstanding ability for overall searching with rough set theory. A fitness function was proposed and applied to the genetic algorithm, which accelerated the speed of convergence. The detailed algorithm and the computation process were presented for practical purpose. The simulation results show that the proposed approach has good searching ability and high restraining speed and can achieve efficient attribute reduction.
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
Pawlak, Z.: Rough sets–theoretical aspects of reasoning about data, pp. 9–30. Kluwer Academic Publishers, Dordrecht (1991)
Bilski, P., Wojciechowski, J.M.: Rough-Sets-Based Reduction for Analog Systems Diagnostics. IEEE Transactions on Instrumentation and Measurement 60(3), 880–890 (2011)
Peng, M.-F., He, Y.-G., Wang, Y.-N.: Synthetic Intelligence Based Fault Diagnosis of Analog Circuit. In: Proceedings of the CSEE, vol. 26(3), pp. 19–24 (2006)
Pawlak, Z.: Rough set theory and its application to data analysis. Cybernetics and Systems 29(9), 661–668 (1998)
Hu, X.H.: Mining knowledge rules from databases-a rough set approach. In: Proceedings of IEEE International Conference on Data Engineering, Los Alamitos, pp. 96–105 (1996)
Wang, S.K.M., Ziarko, W.: One optimal decision rules in decision tables. Bulletin of Polish Academy of Sciences 33(6), 676–693 (1985)
Tao, Z., Xu, B.-D.: Knowledge Reduction of Rough Set Theory Based on GA. Systems Engineering 27(4), 116–122 (2003)
Ren, Y.-G., Wang, Y.: Rough Set Attribute Reduction Algorithm Based on GA. Mini-Micro Systems 27(5), 862–865 (2005)
Swiniarski, R.W., Skowmn, A.: Rough set methods in feature selection and recognition. Pattern Recognition Letters 24, 833–849 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shen, M., Peng, M., Yuan, H. (2012). Rough Set Attribute Reduction Based on Genetic Algorithm. In: Zeng, D. (eds) Advances in Information Technology and Industry Applications. Lecture Notes in Electrical Engineering, vol 136. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-26001-8_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-26001-8_17
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-26000-1
Online ISBN: 978-3-642-26001-8
eBook Packages: EngineeringEngineering (R0)