×
Zhang, Wenxiu; Qiu, Guofang; Wu, Weizhi

A general approach to attribute reduction in rough set theory. (English) Zbl 1118.68669

Sci. China, Ser. F. 50, No. 2, 188-197 (2007).
Summary: The concept of a consistent approximation representation space is introduced. Many types of information systems can be treated and unified as consistent approximation representation spaces. At the same time, under the framework of this space, the judgment theorem for determining consistent attribute set is established, from which we can obtain the approach to attribute reductions in information systems. Also, the characterizations of three important types of attribute sets (the core attribute set, the relative necessary attribute set and the unnecessary attribute set) are examined.

Cited in 4 Documents

MSC:

68T30 Knowledge representation

Keywords:

rough sets; attribute reduction; information systems; approximation representation spaces
PDF BibTeX XML Cite
\textit{W. Zhang} et al., Sci. China, Ser. F 50, No. 2, 188--197 (2007; Zbl 1118.68669)
Full Text: DOI

References:

[1] Pawlak Z. Rough sets. Int J Comp Inf Sci, 1982, 11: 341–356 · Zbl 0501.68053
[2] Pawlak Z. Rough Sets – Theoretical Aspects of Reasoning about Data. Dordrecht: Kluwer Academic Publishers, 1991 · Zbl 0758.68054
[3] Kryszkiewicz M. Comparative study of alternative types of knowledge reduction in insistent systems. Int J Intel Syst, 2001, 16: 105–120 · Zbl 0969.68146
[4] Zhang W-X, Leung Y, Wu W-Z. Information Systems and Knowledge Discovery. Beijing: Science Press, 2003
[5] Beynon M. Reducts within the variable precision rough sets model: A further investigation. Eur J Oper Res, 2001, 134: 592–605 · Zbl 0984.90018
[6] Zhang W-X, Mi J-S, Wu W-Z. Approaches to knowledge reductions in inconsistent systems. Int J Intel Syst, 2003, 18: 989–1000 · Zbl 1069.68606
[7] Qiu G-F, Li H-Z, Xu L-D, et al. A knowledge processing method for intelligent systems based on inclusion degree. Expert Syst, 2003, 20(4): 187–195 · Zbl 05653438
[8] Mi J-S, Wu W-Z, Zhang W-X. Approaches to knowledge reduction based on variable precision rough set model. Inf Sci, 2004, 159: 255–272 · Zbl 1076.68089
[9] Zhang M, Wu W-Z. Knowledge reduction in information systems with fuzzy decisions. J Eng Math, 2003, 20(2): 53–58 · Zbl 1159.68580
[10] Leung Y, Wu W-Z, Zhang W-X. Knowledge acquisition in incomplete information systems: a rough set approach. Eur J Oper Res, 2006, 168(1): 164–180 · Zbl 1136.68528
[11] Wu W-Z, Zhang M, Li H-Z, et al. Knowledge reduction in random information systems via Dempster-Shafer theory of evidence. Inf Sci, 2005, 174(3–4): 143–164 · Zbl 1088.68169
[12] Skowron A, Rauszwer C. The discernibility matrices and functions in information systems. In: Slowinski R, ed. Intelligent Decision Support: Handbook of Applications and Advances of the Rough Set Theory. Dordrecht: Kluwer Academic Publishers, 1992. 331–362
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.