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Hesitant Fuzzy Rough Set Approach Based on Optimistic and Pessimistic Strategies |
LI Jianzhuo1 |
1.School of Computer, Baoji University of Arts and Sciences, Baoji 721013 |
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Abstract The existing hesitant fuzzy rough set model does not take the multi-source information into account. To solve this problem, an optimistic strategy based multigranulation hesitant fuzzy rough set model and a pessimistic strategy based multigranulation hesitant fuzzy rough set model are proposed, respectively. The properties of the two models are shown. Finally, the approximate sets under optimistic version and pessimistic version are analyzed by an example of a multi-source information system.
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Received: 29 March 2018
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Fund:Supported by National Natural Science Foundation of China(No.51207002), Special Project of Scientific Research of Education Department of Shaanxi Province(No.15JK1028),University Key Project of Baoji University of Arts and Sciences(No.zk2017004) |
About author:: LI Jianzhuo, master, lecturer. His research interests include pattern recognition. |
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[1]PAWLAK Z. Rough Sets-Theoretical Aspects of Reasoning about Data. Dordrecht, The Netherlands: Kluwer Academic, 1991. [2]LUO G Z, YANG X B. Limited Dominance-Based Rough Set Model and Knowledge Reductions in Incomplete Decision System. Journal of Information Science and Engineering, 2010, 26(6): 2199-2211. [3]HU Q H, CHE X J, ZHANG L, et al. Rank Entropy Based Decision Trees for Monotonic Classification. IEEE Transactions on Knowledge and Data Engineering, 2012, 24(11): 2052-2064. [4]DUBOIS D, PRADE H. Rough Fuzzy Sets and Fuzzy Rough Sets. International Journal of General Systems, 1990, 17(2/3): 191-209. [5]TORRA V. Hesitant Fuzzy Sets. International Journal of Intelligent Systems, 2010, 25(6): 529-539. [6]王宝丽,梁吉业,胡运红.基于粒计算的犹豫模糊多准则决策方法.模式识别与人工智能, 2016, 29(3): 252-262. (WANG B L, LIANG J Y, HU Y H. Granular Computing Based Hesitant Fuzzy Multi-criteria Decision Making. Pattern Recognition and Artificial Intelligence, 2016, 29(3): 252-262.) [7]YANG X B, SONG X N, QI Y S, et al.Constructive and Axiomatic Approaches to Hesitant Fuzzy Rough Set. Soft Computing(A Fusion of Foundations, Methodologies and Applications), 2014, 18(6): 1067-1077. [8]QIAN Y H, LIANG J Y. Rough Set Method Based on Multi-granulations // Proc of the 5th IEEE International Conference on Cognitive Informatics. Washington, USA: IEEE, 2006: 297-304. [9]QIAN Y H, LIANG J Y, DANG C Y. Incomplete Multigranulation Rough Set. IEEE Transactions on Systems, Man and Cybernetics(Systems and Humans), 2010, 40(2): 420-431. [10]QIAN Y H, LIANG J Y, WEI W. Pessimistic Rough Decision. Journal of Zhejiang Ocean University(Natural Science), 2010, 29(5): 440-449. [11]QIAN Y H, LIANG J Y, YAO Y Y, et al. MGRS: A Multi-granulation Rough Set. Information Sciences, 2010, 180(6): 949-970. [12]YANG X B, SONG X N, DOU H L, et al. Multi-granulation Rough Set: From Crisp to Fuzzy Case. Annals Fuzzy Mathematics Information, 2011, 1(1): 55-70. [13]XU W H, WANG Q R, ZHANG X T. Multi-granulation Fuzzy Rough Sets in a Fuzzy Tolerance Approximation Space. Internatio-nal Journal of Fuzzy Systems, 2011, 13(4): 246-259. [14]MIN F, HE H P, QIAN Y H, et al.Test-Cost-Sensitive Attribute Reduction. Information Sciences, 2011, 181(22): 4928-4942. [15]YANG X B, QI Y, YU H L, et al.Updating Multigranulation Rough Approximations with Increasing of Granular Structures. Knowledge-Based Systems, 2014, 64: 59-69. [16]YANG X B, QI Y S, SONG X N, et al.Test Cost Sensitive Multigranulation Rough Set: Model and Minimal Cost Selection. Information Sciences, 2014, 250: 184-199. [17]KUMAR S S, INBARANI H H. Optimistic Multi-granulation Rou-gh Set Based Classification for Medical Diagnosis. Procedia Computer Science, 2015, 47: 374-382. [18]JU H R, YANG X B, QI Y S, et al. Dynamic Updating Multigra-nulation Fuzzy Rough Set: Approximations and Reducts. International Journal of Machine Learning and Cybernetics, 2014, 5(6): 981-990. [19]JU H R, YANG X B, DOU H L, et al. Variable Precision Multigranulation Rough Set and Attributes Reduction // PETERS J F, SKOWRON A, LI T R, et al., eds. Transactions on Rough Set VIII. Berlin, Germany: Springer, 2014: 52-68. [20]JU H R, LI H X, YANG X B, et al. Cost-Sensitive Rough Set: A Multi-granulation Approach. Knowledge-Based Systems, 2017, 123: 137-153. [21]QIAN Y H, LIANG X Y, LIN G P, et al. Local Multigranulation Decision-Theoretic Rough Sets. International Journal of Approximate Reasoning, 2017, 82: 119-137. [22]QIAN Y H, LIANG X Y, WANG Q, et al. Local Rough Set: A Solution to Rough Data Analysis in Big Data. International Journal of Approximate Reasoning, 2018, 97: 38-63. [23]KONG Q Z, XU W H. The Comparative Study of Covering Rough Sets and Multi-granulation Rough Sets. Soft Computing, 2018, 22: 1-15. [24]吴伟志,陈超君,李同军,等.不协调多粒度标记决策系统最优粒度的对比.模式识别与人工智能, 2016, 29(12): 1095-1103. (WU W Z, CHEN C J, LI T J, et al. Comparative Study on Optimal Granularities in Inconsistent Multi-granular Labeled Decision Systems. Pattern Recognition and Artificial Intelligence, 2016, 29(12): 1095-1103.) [25]谭安辉,李进金,吴伟志.多粒度粗糙集和覆盖粗糙集间的近似与约简关系.模式识别与人工智能, 2016, 29(8): 691-697. (TAN A H, LI J J, WU W Z. Approximation and Reduction Relationships between Multi-granulation Rough Sets and Covering Rough Sets. Pattern Recognition and Artificial Intelligence, 2016, 29 (8): 691-697.) [26]王佳琪,苗夺谦,张红云.基于可变多粒度概率粗糙集的分类模型.模式识别与人工智能, 2017, 30(8): 710-717. (WANG J Q, MIAO D Q, ZHANG H Y. Classification Model Based on Variable Multi-granulation Probabilistic Rough Set. Pa-ttern Recognition and Artificial Intelligence, 2017, 30 (8): 710-717.) |
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