Abstract
By applying the probability estimation of the unavailable attributes derived from the available attributes to the neighborhood system, the suited degree of each neighbor to a given object is depicted. Therefore, the neighborhood space with guaranteed suited precision is obtained. We show how to shrink the rule search space via VPRS model for this space, and also, we will prove the incredibility degree of decision class is guaranteed by the two-layer thresholds.
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Pawlak Z. (1991) Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht, Vol. 9.
Stenfanowski J., A. Tsoukias, (2001) Incomplete information tables and rough classification. Computational Intelligence, 17:454–466.
Skowron A., Slowinski R., Synak P. (2005) Approximation spaces and information granulation. T. Rough Sets. pages 175–189
Kryszkiewicz M. (1998) Rough set approach to incomplete information system. Information Sciences, 112:39–49.
Slowinski R., Vanderpooten D. (2000) A generalized definition of rough approximation based on similarity. IEEE Transactions on Data and Knowledge Engineering.
Stefanowski J., Tsoukias A. (1999) On the extension of rough sets under incomplete information. In: N. Zhong, A. Skowron, S. Ohsuga, (ads.), New Directions in Rough Sets, Data Mining and Granular Soft Computing. LNAI 1711:73–81.
Ziarko W. (2005) Probabilistic Rough Sets. RSFDGrC. 1:283–293.
Wang G. Y. (2002) Extension of Rough Set Under Incomplete Information Systems. Journal of Computer Research and Development. 39(10): 1238–1243.
Gong Z. T., Sun B. Z., Shao Y. B., Chen D. G., He Q. (2004) Variable Precision Rough Set Model Based on General Relation. Proceedings of the Third International Conference on Machine Learning and Cybernetics, Shanghai. pages 26–29.
Wang J. Y., Zhou G. C. (2005) Variable Precision Rough Set model in Incomplete Information System. Proceeding of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, pages 1883–1887.
Mi J. S., Wu W. Z., Zhang W. X. (2004) Approaches to knowledge reduction based on variable precision rough set model. Information Sciences. 159:255–272.
Lenarcik A., Piasta Z. (1887) Probabilistic rough classifiers with mixture of discrete and continuous attributes. In Lin T. Y., Cerone N.(ed.), Rough sets and Data Mining, Kluwer Academic Publisher, pages 373–390.
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© 2006 International Federation for Information Processing
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Feng, Y., Li, W., Lv, Z., Ma, X. (2006). Probabilistic Approximation under Incomplete Information Systems. In: Shi, Z., Shimohara, K., Feng, D. (eds) Intelligent Information Processing III. IIP 2006. IFIP International Federation for Information Processing, vol 228. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-44641-7_8
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DOI: https://doi.org/10.1007/978-0-387-44641-7_8
Publisher Name: Springer, Boston, MA
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