Abstract
In this paper the definition of part reduction is proposed in information systems to describe the minimal description of a definable set by attributes of the given information system. The part reduction can present more optimum description of single decision class than the existing reductions and relative reductions. It is proven that the core of reduction or relative reduction can be expressed as the union of the cores of part reductions. So a deep insight is presented to the classical reductions and relative reductions of information systems so that a unified framework of the reductions of information systems can be set up. The method of discernibility matrix for computing reductions is also generalized to compute the part reductions in information systems.
This paper is supported by a grant of Tianyuan mathematical foundation of China(A0324613) and a grant of Liaoning Education committee (20161049) of China.
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References
Pawlak, Z.: Rough Sets. Internat. J. Comput. Inform. Sci. 11(5), 341–356 (1982)
Jagielska, I., Matthews, C., Whitfort, T.: An investigation into the application of neural networks, fuzzy logic, genetic algorithms, and rough sets to automated knowledge acquisition for classification problems. Neurocomputing 24, 37–54 (1999)
Kryszkiewicz, M.: Rough set approach to incomplete information systems. Information Sciences 112, 39–49 (1998)
Pawlak, Z.: Rough sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Boston (1991)
Tsumoto, S.: Automated extraction of medical expert system rules from clinical databases based on rough set theory. Information Sciences 112, 67–84 (1998)
Skowron, A., Polkowski, L.: Rough sets in knowledge discovery, vol. 1,2. Springer, Berlin (1998)
Slowinski, R. (ed.): Intelligent decision support: Handbook of applications and advances of the rough sets theory. Kluwer Academic Publishers, Boston (1992)
Kryszkiewicz, M.: Comparative study of alternative type of knowledge reduction in insistent systems. International Joural of intelligent systems 16, 105–120 (2001)
Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: Slowinski, R. (ed.) Intelligent decision support: Handbook of applications and advances of the rough sets theory, Kluwer Academic Publishers, Dordrecht (1992)
Slezak, D.: Searching for dynamic reducts in inconsistent decision tables. In: Proceedings of IPMU 1998, Paris, France, vol. 2, pp. 1362–1369 (1998)
Slezak, D.: Approximate reducts in decision tables. In: Proceedings of IPMU 1996, Granada, Spain, vol. 3, pp. 1159–1164 (1996)
Bazan, J.: A comparison of dynamic and non-dynamic rough set methods for extracting laws from decision system. In: Polkowski, L., Skowron, A. (eds.) Rough sets in knowledge discovery, vol. 1, pp. 321–365. Physica-Verlag, Heidelberg (1998)
Bazan, J., Skowron, A., Synak, P.: Dynamic reducts as a tool for extracting laws from decision tables. In: Raś, Z.W., Zemankova, M. (eds.) ISMIS 1994. LNCS (LNAI), vol. 869, pp. 346–355. Springer, Heidelberg (1994)
Bazan, J., Nguyen, H.S., Nguyen, S.H., Synak, P., Wroblewski, J.: Rough set algorithms in classification problem. In: [Polkowski-Tsumoto-Lin], pp. 49–88 (2000)
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Degang, C. (2004). The Part Reductions in Information Systems. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds) Rough Sets and Current Trends in Computing. RSCTC 2004. Lecture Notes in Computer Science(), vol 3066. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25929-9_57
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DOI: https://doi.org/10.1007/978-3-540-25929-9_57
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