Abstract
The rough set theory was meant as a tool for imprecise and inconsistent information systems. Incomplete information can be also considered as a particular case of imprecise information. Because the rough set theory makes the assumption of completeness of all attributes of input vector, many modifications of this theory were developed describing how to use the incomplete data. This article presents the basic approaches: tolerance relation and non symmetric similarity relation. Furthermore, a new method of supplementing some incomplete objects from an information table has been proposed.
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References
E. Adamus Przeglad metod stosowanych do badan nad niekompletnymi danymi pomiarowymi. VIII Sesja Informatyki, II:387-395, Szczecin, 2003.
Greco S., Matarazzo B., Słowiński R. Dealing with missing data In rough sets analysis of multi-attribute and multi-criteria decision problems. Kluwer Academic Publisher, p. 295-316, 2002.
J. Grzymala-Busse and A. Y. Wang. Modified algorithms lem1 and lem2 for rule induction from with missing attribute values. Proc.of theFifth International Workshop on Rough Sets and Soft Computing (RSSC’97) at the Third Join Conference on Information Sciences (JCIS’97), Research Triangle Park, NC, pages 69-72, March 2-5 1997.
G. King, J. Honaker and A. Joseph Analyzing Incomplete Political Science Data: An Alternative Algorithm for multiple imputation. American Political Science Review, (95) p. 49-69, 2001.
M. Kryszkiewicz. Rough set approach to rules generation from incomplete information systems. ICS Research Report 55/95, Warsaw University of Technology; also in: International Journal of Information Sciences, 1995.
M. Kryszkiewicz Rough set approach to incomplete information system. International Journal of Information Sciences, (112): p. 39-49, 1998.
Prechelt L.: Proben1 – A set of neural network benchmark problems and bench-marking rules, Technical Report, 1994
R. Słowiński and D. Vanderpooten. A generalized definition of rough Approximations based on similarity. IEEE Transactions on Knowledge and Data Engineering, 12: 331-336, march/april 2000.
J. Stefanowski and A. Tsoukias. On the extension of rough sets under incomplete information. New Directions in Rough Sets, Data Mining and Granular-Soft Computing, LNAI 1711, Springer-Verlag, Berlin, 1999.
J. Stefanowski and A. Tsoukias. Incomplete information tables and rough classification. Int. Journal of Computational Intelligence. 2001.
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Adamus, E., Piegat, A. (2007). Predicting Incomplete Data on the Basis of Non Symmetric Similarity Relation. In: Pejaś, J., Saeed, K. (eds) Advances in Information Processing and Protection. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-73137-7_8
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DOI: https://doi.org/10.1007/978-0-387-73137-7_8
Publisher Name: Springer, Boston, MA
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