Abstract
Fuzzy granular structure is a mathematical structure to deal with fuzzy information granules. This paper studies fuzzy granular structure of a fuzzy relation vector and its uncertainty measurement. In the first place, fuzzy granular structure of a fuzzy relation vector is defined by using fuzzy set matrices. In the next place, dependence between fuzzy granular structures and some operators on the fuzzy granular structures base are investigated. After that, algebraic and lattice features of fuzzy granular structures are obtained. Lastly, as an application of fuzzy granular structures, uncertainty measurement for fuzzy relation vectors is discussed and effectiveness analysis of the proposed measures is conducted. These results will be helpful for providing the framework for granular computing of fuzzy relation vectors.
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Zadeh, L.A.: Fuzzy logic equals computing with words. IEEE Trans. Fuzzy Syst. 4, 103–111 (1996)
Zadeh, L.A.: Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst. 90, 111–127 (1997)
Lin, T.Y.: Granular computing on binary relations I: data mining and neighborhood systems. In: Skowron, A., Polkowski, L. (eds.) Rough Sets in Knowledge Discovery, pp. 107–121. Physica-Verlag, Heidelberg (1998)
Lin, T.Y.: Granular computing on binary relations II: rough set representations and belief functions. In: Skowron, A., Polkowski, L. (eds.) Rough Sets in Knowledge Discovery, pp. 121–140. Physica-Verlag, Heidelberg (1998)
Yao, Y.Y.: Granular computing: basic issues and possible solutions. In: Proceedings of the Fifth International Conference on Computing and Information, vol. 1, pp. 186–189 (2000)
Yao, Y.Y.: Information granulation and rough set approximation. Int. J. Intell. Syst. 16, 87–104 (2001)
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. Int. J. Gen. Syst. 31, 331–342 (2002)
Liang, J.Y., Qian, Y.H.: Information granules and entropy theory in information systems. Sci. China (Ser. F) 51, 1427–1444 (2008)
Lin, T.Y.: Granular computing: practices, theories, and future directions. In: Meyers, R.A. (ed.) Encyclopedia of Complexity and Systems Science, pp. 4339–4355. Springer, Berlin (2009)
Zhang, L., Zhang, B.: Theory and application of problem solving-theory and application of granular computing in quotient spaces. Tsinghua University Publishers, Beijing (2007)
Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)
Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11, 341–356 (1982)
Pawlak, Z.: Rough sets: Theoretical aspects of reasoning about data. Kluwer Academic Publishers, Dordrecht (1991)
Ma, J.M., Zhang, W.X., Leung, Y., Song, X.X.: Granular computing and dual Galois connection. Inf. Sci. 177, 5365–5377 (2007)
Wu, W.Z., Leung, Y., Mi, J.S.: Granular computing and knowledge reduction in formal contexts. IEEE Trans. Knowl. Data Eng. 21(10), 1461–1474 (2009)
Qian, Y.H., Liang, J.Y., Dang, C.Y.: Knowledge structure, knowledge granulation and knowledge distance in a knowledge base. Int. J. Approx. Reason. 50, 174–188 (2009)
Li, Z.W., Liu, Y.Y., Li, Q.G., Qin, B.: Relationships between knowledge bases and related results. Knowl. Inf. Syst. 49, 171–195 (2016)
Li, Z.W., Li, Q.G., Zhang, R.R., Xie, N.X.: Knowledge structures in a knowledge base. Expert. Syst. 33(6), 581–591 (2016)
Qian, Y.H., Zhang, H., Li, F.J., Hu, Q.H., Liang, J.Y.: Set-based granular computing: a lattice model. Int. J. Approx. Reason. 55, 834–852 (2014)
Li, Z.W., Huang, D., Liu, X.F., Xie, N.X., Zhang, G.Q.: Information structures in a covering information system. Inf. Sci. 507, 449–471 (2020)
Zhang, G.Q., Li, Z.W., Wu, W.Z., Liu, X.F., Xie, N.X.: Information structures and uncertainty measures in a fully fuzzy information system. Int. J. Approx. Reason. 101, 119–149 (2018)
Cament, L.A., Castillo, L.E., Perez, J.P., Galdames, F.J., Perez, C.A.: Fusion of local normalization and Gabor entropy weighted features for face identification. Pattern Recognit. 47(2), 568–577 (2014)
Delgado, A., Romero, I.: Environmental conflict analysis using an integrated grey clustering and entropy-weight method: a case study of a mining project in Peru. Environ. Modell. Softw. 77, 108–121 (2016)
Hempelmann, C.F., Sakoglu, U., Gurupur, V.P., Jampana, S.: An entropy-based evaluation method for knowledge bases of medical information systems. Expert. Syst. Appl. 46, 262–273 (2016)
Xie, S.D., Wang, Y.X.: Construction of tree network with limited delivery latency in homogeneous wireless sensor networks. Wirel. Pers. Commun. 78(1), 231–246 (2014)
Shannon, C.: A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948)
Wierman, M.J.: Measuring uncertainty in rough set theory. Int. J. Gen. Syst. 28, 283–297 (1999)
Liang, J.Y., Shi, Z.Z., Li, D.Y., Wierman, M.J.: The information entropy, rough entropy and knowledge granulation in incomplete information systems. Int. J. Gen. Syst. 35, 641–654 (2006)
Dai, J.H., Tian, H.W.: Entropy measures and granularity measures for set-valued information systems. Inf. Sci. 240, 72–82 (2013)
Qian, Y.H., Liang, J.Y., Wu, W.Z., Dang, C.Y.: Information granularity in fuzzy binary GrC model. IEEE Trans. Fuzzy Syst. 19(2), 253–264 (2011)
Li, Z.W., Zhang, P.F., Ge, X., Xie, N.X., Zhang, G.Q., Wen, C.F.: Uncertainty measurement for a fuzzy relation information system. IEEE Trans. Fuzzy Syst. 27, 2338–2352 (2019)
Xie, N.X., Liu, M., Li, Z.W., Zhang, G.Q.: New measures of uncertainty for an interval-valued information system. Inf. Sci. 470, 156–174 (2019)
Li, Z.W., Liu, X.F., Dai, J.H., Chen, J.L., Fujita, H.: Measures of uncertainty based on Gaussian kernel for a fully fuzzy information system. Knowl.-Based Syst. 196, 105791 (2020)
Cheng, J., Huang, W., Lam, H.K., Cao, J., Zhang, Y.: Fuzzy-model-based control for singularly perturbed systems with nonhomogeneous Markov switching: a dropout compensation strategy. IEEE Trans. Fuzzy Syst. (2020). https://doi.org/10.1109/TFUZZ.2020.3041588
Cheng, J., Shan, Y., Cao, J., Park, J.H.: Nonstationary control for T-S fuzzy Markovian switching systems with variable quantization density. IEEE Trans. Fuzzy Syst. (2020). https://doi.org/10.1109/TFUZZ.2020.2974440
Xie, N.X., Li, Z.W., Wu, W.Z., Zhang, G.Q.: Fuzzy information granular structures: a further investigation. Int. J. Approx. Reason. 114, 127–150 (2019)
Qian, Y.H., Li, Y., Liang, J.Y., Lin, G.P., Dang, C.Y.: Fuzzy granular structure distance. IEEE Trans. Fuzzy Syst. 23(6), 2245–2259 (2015)
Wang, C.Z., Huang, Y., Shao, M.W., Chen, D.G.: Uncertainty measures for general fuzzy relations. Fuzzy Sets Syst. 360, 82–96 (2019)
Hungerford, T.W.: Algebra. Springer, New York (1974)
Golan, J.S.: Semirings and Their Applications. Kluwer Academic Publishers, Dordrecht (1999)
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)
Salvador, G., Herrera, F.: An extension on “statistical comparisons of classifiers over multiple data sets” for all pairwise comparisons. J. Mach. Learn. Res. 9(12), 2677–2694 (2008)
Dunn, O.J.: Multiple comparisons among means. J. Am. Stat. Assoc. 56(293), 52–64 (1961)
Friedman, M.: A comparison of alternative tests of significance for the problem of m rankings. Ann. Math. Stat. 11(1), 86–92 (1940)
Acknowledgements
The authors would like to thank the editors and the anonymous reviewers for their valuable comments and suggestions, which have helped immensely in improving the quality of the paper. This work is supported by National Natural Science Foundation of China (11971420), Natural Science Foundation of Guangxi (AD19245102, 2020GXNSFAA159155, 2018GXNSFDA294003), Key Laboratory of Software Engineering in Guangxi University for Nationalities (2021-18XJSY-03), Education Reform Project of Yulin Normal University (2020XJJGZD17), Special Scientific Research Project of Young Innovative Talents in Guangxi (2019AC20052), and Research projects of young and Middle-Aged Teachers in Guangxi Universities (2020KY14013, 2020KY14008).
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He, J., Qu, L. & Chen, L. Fuzzy Granular Structures of Fuzzy Relation Vectors. Int. J. Fuzzy Syst. 24, 1406–1424 (2022). https://doi.org/10.1007/s40815-021-01198-4
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DOI: https://doi.org/10.1007/s40815-021-01198-4