Comparative Study of Ordered and Covering Information Systems

• Autorzy: Wang C. , He Q. , Chen D. , Hu Q.
• Języki publikacji: EN

Abstrakty

EN

Covering information systems and ordered information systems are two important types of information systems. In this paper the relationships between covering information systems and ordered information systems are first examined, and it is proved that these two types of information systems are isomorphic under given conditions and can be equivalently transformed into each other. Then, the approach to attribute reduction in ordered information systems is proposed. Based on the isomorphism and equivalence of transformation, the method of attribute reduction in a covering information system can be directly obtained according to the reduction approach in an ordered in- formation system. A practical example is employed to show that the proposed method is an effective technique to deal with complex data sets.

Słowa kluczowe

EN

information systems; coverings; dominance relations; rough sets; attribute reduction

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2013
• Tom: Vol. 123, nr 3
• Strony: 351--363
• Opis fizyczny: Bibliogr. 40 poz.

Twórcy

autor

Wang C.

• Department of Mathematics Bohai university, Jinzhou, 121000, P.R.China

autor

He Q.

• Department of Mathematics and Computer Science Hebei University, Baoding, 071002, PR China

autor

Chen D.

• Department of Mathematics and Physics North China Electric Power University Beijing 102206, P. R. China

autor

Hu Q.

• School of Computer Science and Technology Tianjin University, Tianjin 300072, P.R. China

Bibliografia

• [1] Bonikowski, Z., Bryniarski, E., Wybraniec, U.: Extensions and intentions in the rough set theory, Information Science 107 (1998) 149-167.
• [2] Bryson, N., Mobolurin, A.: An action learning evaluation procedure for multiple criteria decision making problems, European Journal of Operational Research 96 (1996) 379-386.
• [3] Chen, D., Wang, C., Hu, Q.: A new approach to attribute reduction of consistent and inconsistent covering decision systems with covering rough sets, Information Sciences 177 (17) ( 2007) 3500-3518.
• [4] Dembczynski, K., Pindur, R., Susmaga, R.: Generation of exhaustive set of rules within dominance-based rough set approach, Electronic Notes in Theoretical Computer Science 82 (2003) 96-107.
• [5] Fernandez Salido, J. M., Murakami, S.: Rough set analysis of a general type of fuzzy data using transitive aggregations of fuzzy similarity relations, Fuzzy Sets and Systems 139 (2003) 635-660.
• [6] Greco, S., Matarazzo, B., Slowinski, R.: Rough sets theory for multicriteria decision analysis, European Journal of Operational Research 129 (2001) 11-47.
• [7] Greco, S., Matarazzo, B., Slowinski, R.: Rough sets methodology for sorting problems in presence of multiple attributes and criteria, European Journal of Operational Research 138 (2002) 247-259.
• [8] Hu, Q., Pedrycz, W., Yu, D. and Lang, J.: Selecting discrete and continuous features based on neighborhood decision error minimization, IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 40 (2010) 137-50.
• [9] Hu, Q., Yu, D., Liu, J., Wu, C.: Neighborhood rough set based heterogeneous feature subset selection, Information Sciences 178 (2008) 3577-3594.
• [10] Iyer, N.S.: A family of dominance rules for multi-attribute decision-making under uncertainty, IEEE Transactions on Systems, Man, and Cybernetics Part A 33 (2003) 441-450.
• [11] 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 (1999) 37-54.
• [12] Jeon, G., Kim, D., Jeong, J.: Rough sets attributes reduction based expert system in interlaced video sequences, IEEE Transactions on Consumer Electronics 52 (4) (2006) 1348-1355.
• [13] Jensen, R., Shen, Q.: Semantics-preserving dimensionality reduction: rough and fuzzy rough based approaches, IEEE Transactions on Knowledge and Data Engineering 16 (2004) 1457-1471.
• [14] Jensen, R., Shen, Q.: Fuzzy-rough sets assisted attribute selection, IEEE Transactions on Fuzzy Systems 15 (1) (2007) 73-89.
• [15] Kryszkiewicz, M.: Rough set approach to incomplete information systems, Information Sciences 112 (1998) 39-49.
• [16] Kryszkiewicz, M.: Rule in incomplete information systems, Information Sciences 113 (1999) 271-292.
• [17] Leung, Y., Wu, W.Z., Zhang, W.X.: Knowledge acquisition in incomplete information systems: a rough set approach, European Journal Operational Research 168 (2006) 164-180.
• [18] Lin, T.Y.: Neighborhood systems and relational database, in: Proceedings of 1988 ACM 16th annual computer science conference, February 23-25, 1988.
• [19] Liu, D., Li, T., Ruan, D., Zou, W.: An Incremental Approach for Inducing Knowledge from Dynamic Information Systems, Fundamenta Informaticae 94 (2009) 245-260
• [20] Liang, J.Y., Dang, C.Y., Chin, K.S., Yam Richard, C.M.: A new method for measuring uncertainty and fuzziness in rough set theory, International Journal of General Systems 31 (4) (2002) 331-342.
• [21] ] Mi, J.S., Leung, Y., Wu, W.Z.: An uncertainty measure in partition-based fuzzy rough sets, International Journal General Systems 34 (2005) 77-90.
• [22] Mordeson, J. N.: Rough set theory applied to (fuzzy) ideal theory, Fuzzy Sets and Systems 121 (2001) 315-324.
• [23] Morsi, N. N., Yakout, M. M.: Axiomatics for fuzzy rough sets, Fuzzy Sets and Systems 100 (1998) 327-342.
• [24] Pomykala, J.A.: Approximation operations in approximation space, Bull. Pol. Acad. Sci. 9-10 (1987) 653-662.
• [25] Pawlak, Z.: Rough sets: theoretical aspects of reasoning about data, Kluwer Academic Publishing, Dordrecht, 1991.
• [26] Pawlak, Z., Skowron, A.: Rudiments of rough sets, Information Science 177 (2007) 3-27.
• [27] Qian, Y., Liang, J., Pedrycz, W., Dang, C.: Positive approximation: an accelerator for attribute reduction in rough set theory, Artificial Intelligence 174 (2010) 597-618.
• [28] Qian, Y.H., Dang, C.Y., Liang, J.Y.: Set-valued ordered information systems, Information Sciences 179 (2009) 2809-2832.
• [29] Shao, M.W., Zhang, W.X.: Dominance relation and rules in an incomplete ordered information system, International Journal of Intelligent Systems 20 (2005) 13-27.
• [30] Skowron, A., Stepaniuk, J.: Tolerance approximation spaces, Fundam. Inform. 27 (1996) 245-253.
• [31] Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems, in: R. Slowinski (Ed.), Intelligent Decision Support-Handbook of Applications and Advances of Rough Sets Theory, Kluwer Academic Publishers, Boston, 1992, pp. 331-362.
• [32] Slowinski, R., vanderpooten, D.: A generalized definition of rough approximations based on similarity, IEEE Trans. Data Knowledge Eng. 2 (2000) 331-336.
• [33] Tsang, E. C.C., Chen, D., Daniel, S. Y.: Approximations and reducts with covering generalized rough sets, Computers and Mathematics with Applications 56 (2008) 279-289.
• [34] Wang, C., Wu, C., Chen, D., Hu, Q.: Communicating between Information Systems, Information Sciences178 (2008) 3228-3239.
• [35] Wang, C., Wu, C., Chen, D.: A systematic study on attribute reduction with rough sets based on general binary relations, Information Sciences 178 (2008) 2237- 2261.
• [36] Wu, W., Zhang, W., Li, H.: Knowledge acquisition in incomplete fuzzy information systems via rough set approach, Expert Systems 20 (2003) 280-286.
• [37] Yao, Y.Y.: Relational interpretations of neighborhood operators and rough set approximation operators, Information Sciences, 101(1998) 239-259.
• [38] Zakowski, W.: Approximations in the space , Demonstratio Mathematica 16 (1983) 761-769.
• [39] Zhu, W., Wang, F.: Reduction and axiomization of covering generalized rough sets, Information Sciences 152 (2003) 217-230.
• [40] Zhu, W.: Topological approaches to covering rough sets, information Sciences 177 (2007) 1892-1915.