Abstract
Attribute reduction is a challenging problem in areas such as pattern recognition, machine learning and data mining. One essence of the rough set theory is knowledge reduction. Computing the minimal knowledge reduction has been proved to be a NP hard problem. Firstly, a concept of approximate reduction based on conditional information entropy in decision table is introduced. Secondly, a novel algorithm for approximate reduction is presented. Finally, experiments are carried out on various databases and the results show that the proposed algorithm is valid and efficient.
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
Komorowski, J., Pawlak, Z., Polkowski, L., Skowron, A.: Rough sets: A tutorial. In: Pal, S.K., Skowron, A. (eds.) Rough Fuzzy Hybridization-A New Trend in Ecision Making, pp. 3–98. Springer, Heidelberg (1999)
Pawlak, Z.: Information Systems: Theoretical Foundations. WNT (1983) (in Polish)
Pawlak, Z.: Rough sets-Theoretical aspects of reasoning about data. Kluwer, Dordrecht (1991)
Skowron, A.: Extracting laws from decision tables. Computational Intelligence 11(2), 371–388 (1995)
Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: Slowiński, R. (ed.) Intelligent Decision Support. Handbook of Applications and Advances of the Rough Set Theory, pp. 311–362. Kluwer, Dordrecht (1992)
Slezak, D.: Approximate reducts in decision tables, Research report, Institute of Computer Science. Warsaw University of Technology (1995)
Slezak, D.: Foundations of entropy-based Bayesian networks: theoretical results & rough set based extraction from data. In: IPMU 2000, Proceedings of the 8th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Madrid, Spain, vol. 1, pp. 248–255 (2000)
Slezak, D.: Approximate entropy reducts. Fundamenta Informaticae 53(3-4), 365–390 (2002)
Wang, G.Y., Yu, H., Yang, D.C.: Decision table reduction based on conditional information entropy. Chinese Journal of Computer 25(7), 759–766 (2002)
Wang, G.Y., Zhao, J., An, J.J.: A comparative study of algebra viewpoint and information viewpoint in attribute reduction. Fundamenta Informaticae 68(3), 289–301 (2005)
Wu, S.X., Li, M.Q., Huang, W.T., Liu, S.F.: An improved heuristic algorithm of attribute reduction in rough set. Journal of System Sciences and Information 2(3), 557–562 (2004)
Slezak, D.: Approximate reducts in decision tables. In: Proc. of IPMU 1996, Granada, Spain, vol. 3, pp. 1159–1164 (1996)
Thangavel, K., Pethalakshmi, A.: Dimensionality reduction based on rough set theory. Applied Soft Computing 9(1), 1–12 (2009)
Wang, J.-y., Zhou, J.: Research of reduct feature sin the variable precision rough set model. Neurocomputing 72(10-12), 2643–2648 (2009)
Yang, T., Li, Q.: Reduction about approximation spaces of covering generalized rough sets. International Journal of Approximate Reasoning 51(3), 335–345 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, B., Li, Y., Li, L., Yu, Y. (2010). An Approximate Reduction Algorithm Based on Conditional Entropy. In: Zhu, R., Zhang, Y., Liu, B., Liu, C. (eds) Information Computing and Applications. ICICA 2010. Communications in Computer and Information Science, vol 106. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16339-5_42
Download citation
DOI: https://doi.org/10.1007/978-3-642-16339-5_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16338-8
Online ISBN: 978-3-642-16339-5
eBook Packages: Computer ScienceComputer Science (R0)