Several Approaches to Attribute Reduction in Variable Precision Rough Set Model
In this paper, we discuss attribute reduction in variable precision rough set model. We consider several kinds of reducts preserving some of lower approximations, upper approximations, boundary regions and the unpredictable region. We show relations among those kinds of reducts. Moreover we discuss logical function representations of the preservation of lower approximations, upper approximations, boundary regions and the unpredictable region as a basis for reduct calculation. By those discussions, the great difference between the analysis using variable precision rough sets and the classical rough set analysis is emphasized.
KeywordsLogical Function Lower Approximation Decision Table Decision Class Discernibility Matrix
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- 2.Grzymala-Busse, J.W.: LERS – A system for learning from examples based on rough sets. In: Slowinski, R. (ed.) Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory, pp. 3–18. Kluwer Academic Publishers, Dordrecht (1992)Google Scholar
- 3.Inuiguchi, M., Tsurumi, M.: On utilization of upper approximations in rough set analysis. In: Pro. Int. Workshop of Fuzzy Syst. & Innovational Comput. (2004) CDROM Google Scholar
- 5.Skowron, A., Rauser, C.M.: The discernibility matrix and functions in information systems. In: Slowinski, R. (ed.) Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory, pp. 331–362. Kluwer Academic Publishers, Dordrecht (1992)Google Scholar
- 6.Ślȩzak, D.: Various approaches to reasoning with frequency based decision reducts: a survey. In: Polkowski, L., Tsumoto, S., Lin, T.Y. (eds.) Rough Set Methods and Applications, pp. 235–285. Physica, Heidelberg (2000)Google Scholar
- 7.Ślȩzak, D., Ziarko, W.: Attribute reduction in the Bayesian version of variable precision rough set model. Electr. Notes Theor. Comput. Sci. 82(4) (2003)Google Scholar