Abstract
We present Rough Set approach to reasoning in information systems with uncertain attributes. A similarity relation between objects is introduced. The relation is a tolerance relation. A reduction of knowledge we propose eliminates only information, which is not essential from the point of view of classification. Our approach is general in the sense it does not assume anything about the semantics of null values and uncertain multivalued attributes. We show how to find decision rules, which have minimal number of conditions and do not increase the degree of non-determinism of the original decision table.
This work has been supported by grant: No 8 T11C 038 08 from the State Committee for Scientific Research
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References
Pawlak Z., Rough Sets: Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Vol. 9, 1991.
Slowiński R., Stefanowski J., Rough Classification in Incomplete Information Systems, Mathematical and Comput. Modelling, Vol. 12, No. 10–11, 1989, pp. 1347–1357.
Słowiński R., Stefanowski J., Handling Various Types of Uncertainty in The Rough Set Approach, in Rough Sets, Fuzzy Sets and Knowledge Discovery (RSKD '93), W. Ziarko (ed.), Springer-Verlag, 1994.
Kryszkiewicz M., Rough Set Approach to Incomplete Information Systems, in Proceedings of Second Annual Joint Conference on Information Sciences, North Carolina, USA, 28 September–1 October, 1995, pp. 194–197
Skowron A., Stepaniuk J., Generalized Approximation Spaces, Proceedings of the Third International Workshop on Rough Sets and Knowledge Discovery RSSC '94, San Jose, USA, 1994, pp. 156–163.
Kryszkiewicz M., Knowledge Reduction in Information Systems, Ph.D. Thesis, Warsaw University of Technology, 1994.
Pawlak Z., Skowron A., A Rough Set Approach to Decision Rules Generation, ICS Research Report 23/93, Warsaw University of Technology, 1993.
Skowron A., Management of Uncertainty in AI: A Rough Set Approach, ICS Research Report 46/93, Warsaw University of Technology, December 1993.
Skowron A., Rauszer C., The Discernibility Matrices and Functions in Information Systems, in Intelligent Decision Support: Handbook of Applications and Advances of Rough Sets Theory, Slowinski R. (ed.), 1992, Kluwer Academic Publisher, pp. 331–362.
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© 1996 Springer-Verlag Berlin Heidelberg
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Kryszkiewicz, M., Rybiński, H. (1996). Reducing information systems with uncertain attributes. In: Raś, Z.W., Michalewicz, M. (eds) Foundations of Intelligent Systems. ISMIS 1996. Lecture Notes in Computer Science, vol 1079. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61286-6_153
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DOI: https://doi.org/10.1007/3-540-61286-6_153
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