Abstract
This paper proposes an approach to representation and analysis of information systems with fuzzy attributes, which combines the variable precision fuzzy rough set (VPFRS) model with the fuzzy flow graph method. An idea of parameterized approximation of crisp and fuzzy sets is presented. A single ε-approximation, which is based on the notion of fuzzy rough inclusion function, can be used to express the crisp approximations in the rough set and variable precision rough set (VPRS) model. A unified form of the ε-approximation is particularly important for defining a consistent VPFRS model. The introduced fuzzy flow graph method enables alternative description of decision tables with fuzzy attributes. The generalized VPFRS model and fuzzy flow graphs, taken together, can be applied to determining a system of fuzzy decision rules from process data.
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References
Bandler, W., Kohout, L.: Fuzzy Power Sets and Fuzzy Implication Operators. Fuzzy Sets and Systems 4, 13–30 (1980)
Burillo, P., Frago, N., Fuentes, R.: Inclusion Grade and Fuzzy Implication Operators. Fuzzy Sets and Systems 114, 417–429 (2000)
Chen, S.M., Yeh, M.S., Hsiao, P.Y.: A Comparison of Similarity Measures of Fuzzy Values. Fuzzy Sets and Systems 72, 79–89 (1995)
Cornelis, C., Van der Donck, C., Kerre, E.: Sinha-Dougherty Approach to the Fuzzification of Set Inclusion Revisited. Fuzzy Sets and Systems 134, 283–295 (2003)
De Baets, B., De Meyer, H., Naessens, H.: On Rational Cardinality-based Inclusion Measures. Fuzzy Sets and Systems 128, 169–183 (2002)
Dubois, D., Prade, H.: Putting Rough Sets and Fuzzy Sets Together. In: Słowiński, R. (ed.) Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory, pp. 203–232. Kluwer Academic Publishers, Dordrecht (1992)
Fernández 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, 635–660 (2003)
Greco, S., Matarazzo, B., Słowiński, R.: Rough Set Processing of Vague Information Using Fuzzy Similarity Relations. In: Calude, C.S., Paun, G. (eds.) Finite Versus Infinite — Contributions to an Eternal Dilemma, pp. 149–173. Springer, Heidelberg (2000)
Greco, S., Pawlak, Z., Słowiński, R.: Generalized Decision Algorithms, Rough Inference Rules, and Flow Graphs. In: Alpigini, J.J., et al. (eds.) RSCTC 2002. LNCS (LNAI), vol. 2475, pp. 93–104. Springer, Heidelberg (2002)
Greco, S., Pawlak, Z., Słowiński, R.: Bayesian Confirmation Measures within Rough Set Approach. In: Tsumoto, S., et al. (eds.) RSCTC 2004. LNCS (LNAI), vol. 3066, pp. 264–273. Springer, Heidelberg (2004)
Greco, S., Matarazzo, B., Słowiński, R.: Rough Membership and Bayesian Confirmation Measures for Parameterized Rough Sets. In: Ślęzak, D., et al. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3641, pp. 314–324. Springer, Heidelberg (2005)
Inuiguchi, M.: Generalizations of Rough Sets: From Crisp to Fuzzy Cases. In: Tsumoto, S., et al. (eds.) RSCTC 2004. LNCS (LNAI), vol. 3066, pp. 26–37. Springer, Heidelberg (2004)
Katzberg, J.D., Ziarko, W.: Variable Precision Extension of Rough Sets. Fundamenta Informaticae 27, 155–168 (1996)
Klir, G.J., Folger, T.A.: Fuzzy Sets, Uncertainty, and Information. Prentice-Hall, Englewood Cliffs (1988)
Lin, T.Y.: Coping with Imprecision Information — Fuzzy Logic. In: Downsizing Expo, Santa Clara Convention Center (1993)
Mieszkowicz-Rolka, A., Rolka, L.: Variable Precision Rough Sets: Evaluation of Human Operator’s Decision Model. In: Sołdek, J., Drobiazgiewicz, L. (eds.) Artificial Intelligence and Security in Computing Systems, pp. 33–40. Kluwer Academic Publishers, Dordrecht (2003)
Mieszkowicz-Rolka, A., Rolka, L.: Variable Precision Fuzzy Rough Sets Model in the Analysis of Process Data. In: Ślęzak, D., et al. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3641, pp. 354–363. Springer, Heidelberg (2005)
Nakamura, A.: Application of Fuzzy-Rough Classifications to Logics. In: Słowiński, R. (ed.) Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory, pp. 233–250. Kluwer Academic Publishers, Dordrecht (1992)
Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)
Pawlak, Z.: Decision Algorithms, Bayes’ Theorem and Flow Graphs. In: Rutkowski, L., Kacprzyk, J. (eds.) Advances in Soft Computing, pp. 18–24. Physica-Verlag, Heidelberg (2003)
Pawlak, Z.: Flow Graphs and Data Mining. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets III. LNCS, vol. 3400, pp. 1–36. Springer, Heidelberg (2005)
Pawlak, Z.: Flow Graphs and Data Mining. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets III. LNCS, vol. 3400, pp. 1–36. Springer, Heidelberg (2005)
Peters, J.F., Skowron, A. (eds.): Transactions on Rough Sets III. LNCS, vol. 3400. Springer, Heidelberg (2005)
Polkowski, L.: Toward Rough Set Foundations. Mereological Approach. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds.) RSCTC 2004. LNCS (LNAI), vol. 3066, pp. 8–25. Springer, Heidelberg (2004)
Radzikowska, A.M., Kerre, E.E.: A Comparative Study of Fuzzy Rough Sets. Fuzzy Sets and Systems 126, 137–155 (2002)
Skowron, A., Stepaniuk, J.: Tolerance Approximation Spaces. Fundamenta Informaticae 27, 245–253 (1996)
Ślęzak, D., Ziarko, W.: Variable Precision Bayesian Rough Set Model. In: Wang, G., et al. (eds.) RSFDGrC 2003. LNCS (LNAI), vol. 2639, pp. 312–315. Springer, Heidelberg (2003)
Ślęzak, D., Wang, G., Szczuka, M., Düntsch, I., Yao, Y. (eds.): RSFDGrC 2005. LNCS (LNAI), vol. 3641. Springer, Heidelberg (2005)
Ślęzak, D.: Rough Sets and Bayes Factor. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets III. LNCS, vol. 3400, pp. 202–229. Springer, Heidelberg (2005)
Słowiński, R. (ed.): Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory. Kluwer Academic Publishers, Dordrecht (1992)
Tsumoto, S., et al. (eds.): RSCTC 2004. LNCS (LNAI), vol. 3066. Springer, Heidelberg (2004)
Yager, R.R., Filev, D.P.: Essentials of Fuzzy Modelling and Control. John Wiley & Sons, Chichester (1994)
Liu, W.N., Yao, J., Yao, Y.: Rough Approximations under Level Fuzzy Sets. In: Tsumoto, S., et al. (eds.) RSCTC 2004. LNCS (LNAI), vol. 3066, pp. 78–83. Springer, Heidelberg (2004)
Zadeh, L.: Fuzzy Sets. Information and Control 8, 338–353 (1965)
Ziarko, W.: Variable Precision Rough Sets Model. Journal of Computer and System Sciences 46, 39–59 (1993)
Ziarko, W.: Probabilistic Rough Sets. In: Ślęzak, D., et al. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3641, pp. 283–293. Springer, Heidelberg (2005)
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Mieszkowicz-Rolka, A., Rolka, L. (2007). On Representation and Analysis of Crisp and Fuzzy Information Systems. In: Peters, J.F., Skowron, A., Düntsch, I., Grzymała-Busse, J., Orłowska, E., Polkowski, L. (eds) Transactions on Rough Sets VI. Lecture Notes in Computer Science, vol 4374. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71200-8_12
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