Abstract
F-rough sets are the first dynamical rough set model for a family of information systems (decision systems). This chapter investigates vagueness and uncertainty from the viewpoints of F-rough sets. Some indexes, including two types of F-roughness, two types of F-membership-degree and F-dependence degree etc., are defined. Each of these indexes may be a set of number, not like other vague and uncertain indexes in Pawlak rough sets. These indexes extend those of Pawlak rough sets, and indicate vagueness and uncertainty in a family of information subsystems (decision subsystems). Moreover, these indexes themselves also include vagueness and uncertainty, namely, vagueness of vagueness and uncertainty of uncertainty. Further, we investigate some interesting properties of these indexes.
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References
An, A., Shan, N., Chan, C., et al.: Discovering rules for water demand prediction: an enhanced rough-set approach. Eng. Appl. Artif. Intell. 9(6), 645–653 (1996)
Bazan, G.J.: A comparison of dynamic non-dynamic rough set methods for extracting laws from decision tables. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery 1: Methodology and Applications, pp. 321–365. Springer, Heidelberg (1998)
Bazan G.J., Nguyen H.S., Nguyen, S.H., et al.: Rough set algorithms in classification problem. In: Polkowski, L., Tsumot, S.O, Lin, T.Y. (eds.) Rough Set Methods and Applications, pp. 49–88. Springer, Heidelberg (2000)
Bello, R., Verdegay, J.L.: Rough sets in the soft computing environment. Inf. Sci. 212(3), 1–14 (2012)
Deng, D.Y., Huang, H.K., Dong, H.B.: Rough sets based on accessible relation. J. Beijing Jiaotong Univ. 30(5), 19–23 (2006). (in Chinese)
Deng, D.Y., Yan, D.X., Wang, J.Y.: Parallel reducts based on attribute significance. In: Ju, J., Greco, S., Lingras, P., et al. (eds.) Rough Set and Knowledge Technology, pp. 336–343. Springer, Heidelberg (2010)
Deng, D.Y., Chen, L.: Parallel reducts and \(F\)-rough sets. In: Wang, G., Li, D., Yao, Y.Y., et al. (eds.) Cloud Model and Granular Computer, pp. 210–228. Science Press, Beijing (2012). (in Chinese)
Deng, D.Y., Pei, M.H., Huang, H.K.: The F-rough sets approaches to the measures of concept drift. J. Zhejiang Norm. Univ. Nat. Sci. 36(3), 303–308 (2013). (in Chinese)
Deng, D.Y., Xu, X.Y., Huang, H.K.: Concept drifting detection for categorical evolving data based on parallel reducts. J. Comput. Res. Dev. 52(5), 1071–1079(2015) (in Chinese)
Deng, D.Y., Miao, D.Q., Huang, H.K.: Analysis of concept drifting and uncertainty in an information table. J. Comput. Res. Dev. 53(11), 2607–2612 (2016). (in Chinese)
Deng, D.Y., Huang, H.K.: Double-level absolute reduction for multi-granulation rough sets. Pattern Recognit. Artif. Intell. 29(11), 969–975 (2016). (in Chinese)
Deng, D.Y, Xue, H.H, Miao, D.Q., et al.: Study on criteria of attribute reduction and information loss of attribute reduction. Acta Eletron. Sin. 45(2), (2017). (in Chinese)
Dubois, D., Prade, H.: Rough fuzzy sets and fuzzy rough sets. Int. J. Gen. Syst. 17(2), 191–209 (1990)
Katzberg, J.D., Ziarko, W.: Variable precision extension of rough set. Foundamenta Inform. 27(2–3), 155–168 (1996)
Li, D., Meng, H., Shi, X.: Membership clouds and membership cloud generators. J. Comput. Res. Dev. 32(6), 15–20 (1995). (in Chinese)
Li, D., Du, Y.: Artificial Intelligence with Uncertainty. National Defence Industry Press, Beijing (2005)
Liu, J., Hu, Q., Yu, D.: A weighted rough set based method developed for class imbalance learning. Inf. Sci. 178(4), 1235–1256 (2008)
Mendel, J.M., John, R.I.: Type-2 fuzzy sets made simple. IEEE Trans. Fuzzy Syst. 10(2), 117–127 (2002)
Pawlak, Z.: Rough sets. Int. J. Inf. Comput. Sci. 11(5), 341–356 (1982)
Pawlak, Z.: Rough Sets-Theoretical Aspect of Reasoning About Data. Kluwer Academic Publishers, Dordrecht (1991)
Pawlak, Z.: Vagueness and uncertainty: a rough set perspective. Comput. Intell. 11(2), 227–232 (1995)
Pawlak, Z., Skowron, A.: Rudiments of rough sets. Inf. Sci. 177(1), 3–27 (2007)
Pawlak, Z., Skowron, A.: Rough sets: some extensions. Inf. Sci. 177(1), 28–40 (2007)
Pawlak, Z., Wong, S.K.M., Ziarko, W.: Rough sets: probabilistic versus deterministic approach. Int. J. Man Mach. Stud. 29(1), 81–95 (1988)
Russell, B.: Vagueness. Aust. J. Philos. 1, 84–92 (1923)
Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton, NJ (1976)
Slezak, D.: The rough bayesian model for distributed decision systems. In: Tsumoto, S., Slowinski, R., Komorowski, H.J. (eds.) Rough Sets and Current Trends in Computing (RSCTC2004), pp. 384–393. Springer, Berlin (2004)
Slezak, D.: Rough sets and Bayes factor. Transaction on Rough Sets III, LNAI 3400, pp. 202–229 (2005)
Slezak, D., Ziarko, W.: Attribute reduction in the bayesian version of variable precision rough set model. Electron. Notes Theor. Comput. Sci. 82(4), 1–11 (2003)
Slezak, D., Ziarko, W.: The investigation of the bayesian rough set model. Int. J. Approx. Reason. 40(1–2), 81–91 (2005)
Wong, S.K.M., Ziarko, W., Wu, J.: Comparison of the probabilistic approximate classification and the fuzzy set model. Fuzzy Sets Syst. 21(3), 357–362 (1987)
Yao, Y.Y.: Probabilistic approaches to rough sets. Expert Syst. 20(5), 287–297 (2003)
Yao, Y.Y., Wong, S.K.M.: A decision theoretic framework for approximating concepts. Int. J. Man Mach. Stud. 37(6), 793–809 (1992)
Yao, Y.Y., Wong, S.K.M., Lingras, P.: A decision-theoretic rough set model. In: Ras, Z.W., Zemankova, M., Emrich, M.L. (eds.) Methodologies for Intelligent Systems, vol. 5, pp. 17–24. North-Holland, New York (1990)
Yao, Y.Y., Zhao, Y.: Attribute reduction in decision-theoretic rough set models. Inf. Sci. 178(17), 3356–3373 (2013)
Zadeh, L.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)
Zhu, W., Wang, F.: Reduction and axiomization of covering generalized rough sets. Inf. Sci. 152(1), 217–230 (2003)
Zhu, W.: Topological approaches to covering rough sets. Inf. Sci. 177(6), 1499–1508 (2007)
Zhu, W.: Generalized rough Sets based on relations. Inf. Sci. 177(22), 4997–5011 (2007)
Zhu, W., Wang, F.: On three types of covering rough sets. IEEE Trans. Knowl. Data Eng. 19(8), 1131–1144 (2007)
Ziarko, W.: Variable precision rough sets model. J. Comput. Syst. Sci. 46(1), 39–59 (1993)
Ziarko, W.: Acquisition of hierarchy-structured probabilistic decision tables and rules from data. Expert Syst. 1(20), 305–310 (2003)
Ziarko, W.: Probabilistic rough sets. In: Slezak, D., Wang, G.Y., Szczuka, M.S., et al. (eds.) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC2005), pp. 283–293. Springer, Heidelberg (2005)
Acknowledgements
The work is supported by National Natural Science Foundation of China (Nos 61473030), Zhejiang Provincial Natural Science Foundation of China (Nos LY15F020012) and Zhejiang Provincial Top Discipline of Cyber Security at Zhejiang Normal University.
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Deng, D., Huang, H. (2017). Vagueness and Uncertainty: An F-Rough Set Perspective. In: Wang, G., Skowron, A., Yao, Y., Ślęzak, D., Polkowski, L. (eds) Thriving Rough Sets. Studies in Computational Intelligence, vol 708. Springer, Cham. https://doi.org/10.1007/978-3-319-54966-8_15
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