Rough Set Model and Decision Research in Intuitionistic Fuzzy Information System Based on Weighted Multi-granulation
JI Xia, ZHAO Peng, YAO Sheng
1.Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei 2300393 2.College of Computer Science and Technology, Anhui University, Hefei 230601
Abstract By analyzing the limitation of the current multi-granulation intuitionistic fuzzy rough set(MGIFRS), a MGIFRS based on weighted granulations is presented in this paper. Firstly, the properties of the presented MGIFRS are analyzed. Three intuitionistic fuzzy rough sets, MGIFRS based on weighted granulations, optimistic MGIFRS and pessimistic MGIFRS, are compared to declare their relationships, and the relationship of uncertainty measurements under these three kinds of MGIFRS are also discussed. Then, the certainty factor and support factor of the decision rule are defined. A rule acquisition method is provided to make up for the shortcomings of the existing MGIFRS. Finally, an decision-making example is utilized to verify the validity of the proposed MGIFRS based on weighted granulations.
Fund:Supported by National Natural Science Foundation of China(No.61602004,61300057), Natural Science Foundation of Anhui Pro-vince(No.1408085MF122,1508085MF127), Natural Science Foundation of Anhui Higher Education Institutions(No.KJ2017A011,KJ2016A041), Project of Key Laboratory of Intelligent Computing and Signal Processing of Anhui University of Ministry of Education, Project of Information Support Technology Collaborative Innovation Center of Anhui University
JI Xia,ZHAO Peng,YAO Sheng. Rough Set Model and Decision Research in Intuitionistic Fuzzy Information System Based on Weighted Multi-granulation[J]. , 2017, 30(11): 971-982.
[1] SALVATORE G,MATARAZZO B, SLAVINSKI R. Rough Sets Theory for Multicriteria Decision Analysis. European Journal of Ope-rational Research, 2001, 129(1): 1-47. [2] CHEN S J, HWANG C L, BECKMANN M J, et al. Fuzzy Multiple Attribute Decision Making: Methods and Applications. New York, USA: Springer-Verlag, 1992. [3] 张文修,吴伟志,梁吉业,等.粗糙集理论与方法.北京:科学出版社, 2001. (ZHANG W X, WU W Z, LIANG J Y, et al. Rough Set Theory and Method. Beijing, China: Science Press, 2001.) [4] QIAN Y H, LIANG J Y, YAO Y Y, et al. MGRS: A Multi-granulation Rough Set. Information Sciences, 2010, 180(6): 949-970. [5] 桑妍丽,钱宇华.一种悲观多粒度粗糙集中的粒度约简算法.模式识别与人工智能, 2012, 25(3): 361-366. (SANG Y L, QIAN Y H. A Granular Space Reduction Approach to Pessimistic Multi-granulation Rough Sets. Pattern Recognition and Artificial Intelligence, 2012, 25(3): 361-366.) [6] XU W H, WANG Q R. Multi-granulation Fuzzy Rough Sets in a Fuzzy Tolerance Approximation Space. International Journal of Fuzzy Systems, 2011, 13(4): 246-259. [7] LIN G P, QIAN Y H, LI J J. NMGRS: Neighborhood-Based Multigranulation Rough Sets. International Journal of Approximate Reasoning, 2012, 53(7): 1080-1093. [8] QIAN Y H, LI S Y, LIANG J Y, et al. Pessimistic Rough Set Based Decisions: A Multi-granulation Fusion Strategy. Information Sciences, 2014, 264: 196-210. [9] LIU C H, MIAO D Q, QIAN J. On Multi-granulation Covering Rough Sets. International Journal of Approximate Reasoning, 2014, 55(6): 1404-1418. [10] 马 睿,刘文奇.基于集值信息系统的多粒度粗糙集.系统工程与电子技术, 2014, 36(5): 920-925. (MA R, LIU W Q. Multi-granulation Rough Set Model Based on Set-Valued Information System. Systems Engineering and Electro-nics, 2014, 36(5): 920-925.) [11] ATANASSOV K T. Intuitionistic Fuzzy Sets. Fuzzy Sets and Systems, 1986, 20(1): 87-96. [12] LI D F. Multiattribute Decision Making Models and Methods Using Intuitionistic Fuzzy Sets. Journal of Computer and System Sciences, 2005, 70(1): 73-85 [13] XU Z S, YAGER R R. Some Geometric Aggregation Operators Based on Intuitionistic Fuzzy Sets. International Journal of General Systems, 2006, 35(4): 417-433. [14] PANKOWSKA A, WYGRALAK M. General IF-Sets with Triangular Norms and Their Applications to Group Decision Making. Information Sciences, 2006, 176(18): 2713-2754. [15]HUANG B, LI H X, WEI D K. Dominance-Based Rough SetModel in Intuitionistic Fuzzy Information Systems. Knowledge-Based Systems, 2012, 28(1): 115-123. [16] HUANG B, WEI D K, LI H X, et al. Using a Rough Set Model to Extract Rules in Dominance-Based Interval-Valued Intuitionistic Fuzzy Information Systems. Information Sciences, 2013, 221(2): 215-229. [17] ZHANG Z M. A Rough Set Approach to Intuitionistic Fuzzy Soft Set Based Decision Making. Applied Mathematical Modelling, 2012, 36(10): 4605-4633. [18] 吴 磊,杨善林,郭 庆.优势关系下直觉模糊目标信息系统的上近似约简.模式识别与人工智能, 2014, 27(4): 300-304. (WU L, YANG S L, GUO Q. Upper Approximation Reduction in Intuitionistic Fuzzy Object Information Systems with Dominance Relations. Pattern Recognition and Artificial Intelligence, 2014, 27(4): 300-304.) [19] 郭 庆,殷 明,吴 磊.直觉模糊信息系统的优势关系及其约简.系统工程与电子技术, 2014, 36(11): 2239-2243. (GUO Q, YIN M, WU L. Dominance Relation and Reduction in Intuitionistic Fuzzy Information System. Systems Engineering and Electronics, 2014, 36(11): 2239-2243.) [20] 郭 庆,吴 磊.多粒度背景下直觉模糊信息系统的粗糙集及其决策.系统工程与电子技术, 2016, 38(2): 347-351. (GUO Q, WU L. Rough Set Model and Decision in Intuitionistic Fuzzy Information System Based on Multi-granulation. Systems Engineering and Electronics, 2016, 38(2): 347-351.) [21] KADZINSKI M, GRECO S, SLOWINSKI R. Robust Ordinal Regression for Dominance-Based Rough Set Approach to Multiple Criteria Sorting. Information Sciences, 2014, 283: 211-228. [22] GRECO S, MATARAZZO B, SLOWINSKI R. Rough Approximation by Dominance Relation. International Journal of Intelligent Systems, 2012, 17(2): 153-171.
2017, Vol. 30 
