Abstract
Fuzzy set theory and rough set theory are effective tools for dealing with incomplete and inaccurate knowledge of information systems. In this paper, for the difference of fuzzy equivalence relation matrix of the attributes in information system, by analyzing the shortcomings of the existing methods, we propose the axiomatic system of fuzzy information filter operators, and give several operational filter operators models, spell out an attribute reduction method based on fuzzy information filter operators (denoted by FIFO-RED for short); Finally, we analyze the characteristics and performance of FIFO-RED by a concrete example. And the results indicate that FIFO-RED can more effectively merge decision consciousness into information processing than FIE-RED, it has strong application value.
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
Radzikowska, A.M., Kerre, E.E.: A Comparative Study of Fuzzy Rough Set. Fuzzy Sets and Systems, 137–156 (2002)
Yu, D.R., Hu, Q.H., Bao, W.: Combining Rough Set Methodology and Fuzzy Clustering for Knowledge Discovery from Quantitative Data. Proceedings of the Chinese Society for Electrical Engineering, 205–210 (2004)
Zhu, Y.L., Wu, L.Z., Li, X.Y.: Synthesized Diagnosis on Transformer Faults Based on Bayesian Classifier and Rough Set. Proceedings of the Chinese Society for Electrical Engineering, 159–165 (2005)
Wang, Y.Q., Li, F.C., Li, H.M.: Synthetic Fault Diagnosis Method of Power Transformer Based on Rough Set Theory and Bayesian Network. Proceedings of the Chinese Society for Electrical Engineering, 137–141 (2006)
Sun, Q.Y., Zhang, H.G.: Fault Diagnose Algorithm of Distribution System by Continuous Signals Based on Rough Sets. Proceedings of the Chinese Society for Electrical Engineering, 156–161 (2006)
Xie, H., Cheng, H.Z., Niu, D.X.: Discretization of Continuous Attributes in Rough Set Theory Based on Information Entropy. Chinese Journal of Computers, 1570–1574 (2005)
Jensen, R., Shen, Q.: Semantics-preserving Dimensionality Reduction: Rough and Fuzzy-rough Fuzzy-rough-based Approaches. IEEE Transactions on Knowledge and Data Engineering, 1457–1471 (2004)
Dubois, D., Prade, H.: Rough Fuzzy Sets and Fuzzy Rough Sets. International Journal General Systems, 191–209 (1990)
Hu, Q.H., Yu, D.R., Xie, Z.X., Liu, J.F.: Fuzzy Probabilistic Approximation Spaces and Their Information Measures. IEEE transactions on Fuzzy Systems, 191–201 (2006)
Yeung, D.S., Chen, D.G., Tsang, E.C.C., Lee, J.W.T., Wang, X.Z.: On the Generalization of Fuzzy Rough Sets. IEEE Transactions on Fuzzy Systems, 343–361 (2005)
Hu, Q.H., Yu, D.R., Xie, Z.X.: Information-preserving Hybrid Data Reduction Based on Fuzzy-rough Techniques. Pattern Recognition Letters, 414–423 (2006)
Slowinski, R., Vanderpooten, D.: A Generalized Definition of Rough Approximations Based on Similarity. IEEE Transactions on Knowledge and Data Engineering, 331–336 (2000)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, F., Gao, C., Jin, C. (2008). Attribute Reduction Based on the Fuzzy Information Filter Operators. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2008. Lecture Notes in Computer Science(), vol 5227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85984-0_45
Download citation
DOI: https://doi.org/10.1007/978-3-540-85984-0_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85983-3
Online ISBN: 978-3-540-85984-0
eBook Packages: Computer ScienceComputer Science (R0)