Reducing information systems with uncertain attributes
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.
Key wordsApproximate Reasoning Knowledge Discovery Rough Sets Uncertain Information System Knowledge Representation Reduction
Unable to display preview. Download preview PDF.
- Pawlak Z., Rough Sets: Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Vol. 9, 1991.Google Scholar
- Slowiński R., Stefanowski J., Rough Classification in Incomplete Information Systems, Mathematical and Comput. Modelling, Vol. 12, No. 10–11, 1989, pp. 1347–1357.Google Scholar
- 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.Google Scholar
- 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. 194–197Google Scholar
- 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.Google Scholar
- Kryszkiewicz M., Knowledge Reduction in Information Systems, Ph.D. Thesis, Warsaw University of Technology, 1994.Google Scholar
- Pawlak Z., Skowron A., A Rough Set Approach to Decision Rules Generation, ICS Research Report 23/93, Warsaw University of Technology, 1993.Google Scholar
- Skowron A., Management of Uncertainty in AI: A Rough Set Approach, ICS Research Report 46/93, Warsaw University of Technology, December 1993.Google Scholar
- 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.Google Scholar