基于粗糙集理论的多属性决策方法
|
摘要
随着社会的不断进步和科技的迅猛发展,多属性决策理论及其方法已在管理决策领域得到了广泛的应用。然而,面对信息系统“日益复杂”和“动态变化”的今天,如何调节决策过程中单目标与多目标、静态决策与动态决策间的矛盾；如何制定合理有效的权重及规则挖掘机制；如何从外界环境变更中获取动态决策规则,实现知识的高效更新,已成为现代决策理论所面临的重点和难点。本文在充分考虑管理决策语义环境下,将粗糙集理论引入多属性决策过程,回答了如何从众多粗集模型选取适用的决策模型,以及怎样将这些模型应用到管理决策系统中的问题。进而,分别从属性权重设定和规则获取两个方面由浅入深、循序渐进地探讨粗集多属性决策的理论和方法。
首先,通过综合分析现有权重设定的不足,将粗集理论与信息熵引入多属性决策权重确立中,提出一种基于粗集和信息增益的属性权重获取方法来解决权重的主观性和冗余性的不足,并与其他决策方法(AHP等)进行对比研究。
其次,考虑到经济管理系统的数据大多具有偏好特征,将偏好关系引入概率粗集模型中,提出一种新的基于偏好下的概率度量关系,并分别建立在完全信息和不完全信息条件下的基于偏好关系的概率粗集模型,提出一种适应于管理信息系统的规则获取方法。由于该方法充分考虑到决策尺度的弹性问题和决策属性中“序”的特征,这使得决策结果更具说服力。
再者,考虑当外界环境发生变化后,如何寻找合理有效的动态决策学习模型及策略的角度出发,分别从属性集不变,对象集变化、对象集不变,属性集变化,以及对象集和属性集不变时,属性值变化三个方面来探讨动态粗集多属性决策问题,并提出一系列新的解决动态决策问题的权变方法。实验结果表明,新的方法不仅在决策效率上有较大提高,而且使得决策过程更为直观、简单,这为人们提供了一种新的解决动态决策难题的思路。
本论文基于管理信息系统的视角,结合数学中的粗集理论及计算机相关数据挖掘技术,从简单到复杂、从静态到动态,初步而又系统地建立一套解决多属性决策问题的体系,并在某种程度上弥补了现有多属性决策方法的缺陷,具有一定的理论意义和应用价值。
With the advancement progress of society and the fast development of technology, the researches on the theories and approaches of multiple-attribute decision making have receive great achievements in management science. However, as the result of the information system is becoming more complex and constantly changing all the time in nowadays, the approaches of how to adjust the conflict between single target and multiple targets (criteria), static environment and dynamic environment; how to acquire the reasonable and efficiency weight and rule mining mechanism, how to obtain the dynamic decision rules and updating strategies for generating knowledge, have become the new keystone and difficult in decision making problems. By considering of the semantic environment in management decisions, rough set theory is induced into multiple-attribute decision procedure. The problems of how to choose the property rough set models and how to use them in management decision system is clearly discussed in our paper. In addition, the two problems about weight setting and rules acquirement are proposed step by step to illuminate the theories and approaches of the multiple-decision making, respectively.
Firstly, observed by the lack the weight setting, rough set theory as well as information entropy are induced into multiple-attribute decision making, an approach for attribute weights acquisition based on rough sets theory and information gain is propose to overcome the subjectivity and redundancy, and our approach is also compare with other methods (i.e. AHP).
Secondly, with the insightful gain from the ordinality and inconsistency in real management information system simultaneously, the preference-orders relation is induced into Probabilistic rough sets model (PRS), and a Probabilistic model of strict-dominance-based rough set approaches (P-SDRSA) based on complete information system and incomplete information system are proposed respectively. Due to these approaches consider the flexibility. problem and the "order" character in decision making process, it make the decision result more reasonable and suitable for us to acquire the decision rules in management decision environment.
In addition, considering the changes of environment, three different models and strategies are proposed for the knowledge incremental learning in dynamic decision system, including the following three cases:(1) The object set in the information system evolves over time while the attribute set remains constant; (2) The attribute set in the information system evolves over time while the object set remains constant; (3) The attribute value in the information system evolves over time while the object set and attribute set remain constant. These models give a series of new approaches to deal with the dynamic decision problems. Furthermore, the experiments results not only provide the efficiency of our approaches, but also make the decision process simpler and more clearly, which gives us a new viewpoint to solve the dynamic decision puzzles.
Overall, rough set theory in mathematic and data mining technologies in computer science are integrated into multiple-attribute decision making in this paper, a new approach based on the view of management information system is set up to solve the multiple-attribute decision problems from simple to complex, from static to dynamic, which remedy the defects of classical multiple-attribute decision methods somehow. In a short, these researches have some theoretical significance and clinical value in multiple-attribute decision making studies.
首先,通过综合分析现有权重设定的不足,将粗集理论与信息熵引入多属性决策权重确立中,提出一种基于粗集和信息增益的属性权重获取方法来解决权重的主观性和冗余性的不足,并与其他决策方法(AHP等)进行对比研究。
其次,考虑到经济管理系统的数据大多具有偏好特征,将偏好关系引入概率粗集模型中,提出一种新的基于偏好下的概率度量关系,并分别建立在完全信息和不完全信息条件下的基于偏好关系的概率粗集模型,提出一种适应于管理信息系统的规则获取方法。由于该方法充分考虑到决策尺度的弹性问题和决策属性中“序”的特征,这使得决策结果更具说服力。
再者,考虑当外界环境发生变化后,如何寻找合理有效的动态决策学习模型及策略的角度出发,分别从属性集不变,对象集变化、对象集不变,属性集变化,以及对象集和属性集不变时,属性值变化三个方面来探讨动态粗集多属性决策问题,并提出一系列新的解决动态决策问题的权变方法。实验结果表明,新的方法不仅在决策效率上有较大提高,而且使得决策过程更为直观、简单,这为人们提供了一种新的解决动态决策难题的思路。
本论文基于管理信息系统的视角,结合数学中的粗集理论及计算机相关数据挖掘技术,从简单到复杂、从静态到动态,初步而又系统地建立一套解决多属性决策问题的体系,并在某种程度上弥补了现有多属性决策方法的缺陷,具有一定的理论意义和应用价值。
With the advancement progress of society and the fast development of technology, the researches on the theories and approaches of multiple-attribute decision making have receive great achievements in management science. However, as the result of the information system is becoming more complex and constantly changing all the time in nowadays, the approaches of how to adjust the conflict between single target and multiple targets (criteria), static environment and dynamic environment; how to acquire the reasonable and efficiency weight and rule mining mechanism, how to obtain the dynamic decision rules and updating strategies for generating knowledge, have become the new keystone and difficult in decision making problems. By considering of the semantic environment in management decisions, rough set theory is induced into multiple-attribute decision procedure. The problems of how to choose the property rough set models and how to use them in management decision system is clearly discussed in our paper. In addition, the two problems about weight setting and rules acquirement are proposed step by step to illuminate the theories and approaches of the multiple-decision making, respectively.
Firstly, observed by the lack the weight setting, rough set theory as well as information entropy are induced into multiple-attribute decision making, an approach for attribute weights acquisition based on rough sets theory and information gain is propose to overcome the subjectivity and redundancy, and our approach is also compare with other methods (i.e. AHP).
Secondly, with the insightful gain from the ordinality and inconsistency in real management information system simultaneously, the preference-orders relation is induced into Probabilistic rough sets model (PRS), and a Probabilistic model of strict-dominance-based rough set approaches (P-SDRSA) based on complete information system and incomplete information system are proposed respectively. Due to these approaches consider the flexibility. problem and the "order" character in decision making process, it make the decision result more reasonable and suitable for us to acquire the decision rules in management decision environment.
In addition, considering the changes of environment, three different models and strategies are proposed for the knowledge incremental learning in dynamic decision system, including the following three cases:(1) The object set in the information system evolves over time while the attribute set remains constant; (2) The attribute set in the information system evolves over time while the object set remains constant; (3) The attribute value in the information system evolves over time while the object set and attribute set remain constant. These models give a series of new approaches to deal with the dynamic decision problems. Furthermore, the experiments results not only provide the efficiency of our approaches, but also make the decision process simpler and more clearly, which gives us a new viewpoint to solve the dynamic decision puzzles.
Overall, rough set theory in mathematic and data mining technologies in computer science are integrated into multiple-attribute decision making in this paper, a new approach based on the view of management information system is set up to solve the multiple-attribute decision problems from simple to complex, from static to dynamic, which remedy the defects of classical multiple-attribute decision methods somehow. In a short, these researches have some theoretical significance and clinical value in multiple-attribute decision making studies.
引文
1. Allais. M. The so-called Allais paradox and rational decisions under uncertainty [M]. In: Alliais and Hagen, editors. Expected utility hypotheses and the allais paradox. Holland: Reidel Publishing Company,1979.
2. Alam. S., Ghosh. Shrabonti. Rank by AHP:a Rough approach [C]. In:Proceeding of ISCF, 2002,p185-190.
3. Armstrong. R., Cook. W., Seiford. L. Priority ranking and consensus formation:The case of ties [J]. Management Science,1982,28(6):p 638-645.
4. Arrow. K. Alternative Approaches to the Theory of Choice in Risk-Taking Situations [J]. Econometrica,1951(19):p404-437.
5. Bang. W, Zeungnam. B. New incremental learning algorithm in the framework of rough set theory [J]. International Journal of Fuzzy Systems.1999,1(1):p25-36.
6. Becker. J., Sarin. R. Lottery dependent utility [J]. Management Science,1987(33): p1367-1382.
7. Beynon. M. Reducts within the variable precision rough sets model:a further investigation [J]. European Journal of Operational Research.2001,134(3):p592-605.
8. Blaszczynski. J., Greco S., Slowinski R. et. al:On variable consistency dominance-based rough set approaches [C]. In:Proceeding of RSCTC 2006, LNAI 4259,2006, p191-202.
9. Blaszczynski. J., Greco S., Slowinski. R. et. al:Monotonic Variable Consistency Rough Set Approaches [J], International Journal of Approximate Reasoning,2009,50(7), p979-999.
10. Bodjanova. S. Approximation of fuzzy concepts in decision making [J]. Fuzzy Sets and Systems,1997,85:p23-29.
11. Bonikowski. Z, Bryniarski. E., Wybraniec. U. Extensions and intentions in the rough set theory [J]. Information Science,1998,107:p149-167.
12. Bryniarski. E. Some aspects of decision making under uncertainty [J]. Journal of Statistical Planning and Inference,2006,137(8):p2613-2632.
13. Chan. C. A rough set approach to attribute generalization in data mining [J]. Information Sciences,1998,107:p177-194.
14. Chen. H., Li. T., Qiao. S., Ruan. D. A Rough Set Based Dynamic Maintenance Approach for Approximations in Coarsening and Refining Attribute Values [J]. International Journal of Intelligent System,2010, p1005-1026.
15. Chen. S., Fu. G. Combining fuzzy iteration model with dynamic programming to solve multiobjective multistage decision making problems [J]. Fuzzy Sets and Systems, 2005(152):p499-512.
16. Chew. S., MacCrimmon. K. "Alpha-nu Choice Theory:A generalization of expected utility theory" [J]. Working Paper, No.669, University of British Columbia, Vancouver, 1979.
17. Dimitras. A. Business failure prediction using rough sets [J]. European Journal of Operational Research,1999 (114):p263-280.
18. Dubois. D., Prade. H. Rough fuzzy sets and fuzzy rough sets [J]. Journal of General Systems,1990,17(2-3):p191-209.
19. Duntsch. I, Gediga G. Uncertainty measures of rough set prediction [J]. Journal of Artificial Intelligence,1998,106 (1):p77-107.
20. Dyer. J., Fishburn. P., Wallenius. J., et. al. Multiple criteria decision making, Multi-attribute utility theory:the next ten years [J]. Management Science,1992,38(5): p645-653.
21. Edwards. W. The theory of decision-making [J]. Psychological Bulletin,1954,51: p380-417.
22. Edwards. W. Behavioral decision theory [J]. Annual Review of Psychology,1961,12: p473-498.
23. Fishbern. P. Dominance in SSB utility theory [J]. Journal of Economic Theory,1984(34): p130-148.
24. Fishburn. P., Lavalle. I. Multiattribute expected utility without the archimedean axiom [J]. Journal of Mathematical Psychology,1992,36(4):p573-591.
25. Graham. I., Jones. P., Expert systems:knowledge, unicertainty and decision [M]. London, New York,1980.
26. Greco. S., Matarazzo. B., Slowinski. R. Rough set approach to multi-attribute choice and ranking problems [C]. In:Proceedings of the 12th International Conference on Multiple Criteria Decision Making, Hagen, Springer, Berlin,1997, p318-329.
27. Greco. S., Matarazzo. B., Slowinski. R. Rough approximation of a preference relation by dominance relations [J]. European Journal of Operational Research,1999,117:p 63-83.
28. Greco. S., Matarazzo. B., Slowinski. R., Stefanowski. J.:Variable consistency model of dominance-based rough sets approach [C]. In:Proceeding of RSCTC2000, LNAI2005, 2001,p170-181.
29. Greco. S. Rough set theory for multicriterial decision analysis [J], European Journal of Operational Research,2001,129:p1-47.
30. Grzymala-Busse. J. LERS-a system for learning from examples based on rough sets [M]. In:Intelligent Decision Support:Handbook of Applications and Advances of the Rough Sets Theory, Kluwer Academic Publishers, Boston,1992:p3-18.
31. Grzymala-Busse. J. A new version of the rule induction system LERS [J]. Fundamenta Informaticae,1997,31:p37-39.
32. Grzymala-Busse. J., Siddhaye S. Rough set approaches to rule induction from incomplete data [C]. In:Proceeding of International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems,2004, p923-930.
33. Grzymala-Busse. J. Characteristic relations for incomplete data:A generalization of the indiscernibility relation [J]. Transactions on Rough Sets Ⅳ,2005, p58-68.
34. Guo. S., Wang. Z., Wu. Z., Yan. H. A novel dynamic incremental rules extraction algorithm based on rough set theory [C]. In:Proc Proceedings of the Fourth International Conference on Machine Learning and Cybernetics,2005, p1902-1907.
35. Guan. Y., Wang. H. Set-valued information systems [J]. Information Science,2006,176: p2507-2525.
36. Han. J., Kamber. M. Data Mining:Concepts and techniques (2nd Edition) [M], Morgan Kaufmann,2006, USA.
37. Inuiguchi M., Yoshioka.Y.:Variable-precision dominance-based rough set approach [C]. In:Proceeding of RSCTC 2006, LNAI 4259,2006, p203-212.
38. Jia. J., Dyer. J., Butler. J. Measures of Perceived Risk [J]. Management Science,1999, 45(4):p519-532.
39. Kaliszewski. I. Dynamic parametric bounds on efficient outcomes in interactive multiple criteria decision making problems [J], European Journal of Operational Research,2003, 147:p94-107.
40. Katzberg. J., Ziarko. W. Variable precision extension of rough sets [J]. Fundamenta Informaticae,1996,27(2-3):155-168.
41. Kazimierz. Z. Rough approximation of a preference relation by a multi-attribute stochastic dominance for determinist and stochastic evaluation problems [J]. European Journal of Operational Research,2001,130:p305-314.
42. Kryszkiewicz. M. Rough set approach to incomplete information system [J]. Information Sciences,1998,112:p39-49.
43. Li. L., Lai K. Fuzzy dynamic programming approach to hybrid multi-objective multistage decision making problems [J]. Fuzzy Sets and Systems,2001,17:p13-25.
44. Li. T., Ruan. D, Greet. W. et al. A rough set based characteristic relation approach for dynamic attribute generalization in data mining [J]. Knowledge-Based Systems,2007, 20(2):p485-494.
45. Lin. T. and Liu, Q. Rough approximate operators:Axiomatic rough set theory [M]. In: Rough sets, Fuzzy sets and Knowledge Discover. Springer-Verlag, London,1994, p256-260.
46. Lin. T. and Liu, Q. First-order Rough Logic I:approximate reasoning via rough sets [J]. Fundaments Informaticae,1996,27 (2-3):p137-144.
47. Liu. D., Li. T., Ruan. D., Zou. W. An incremental approach for inducing knowledge from dynamic information systems [J]. Fundamenta Informaticae,2009,94 (2):p245-260.
48. Liu. D., Li. T., Ruan. D., Zhang. J. Incremental Learning Optimization on Knowledge Discovery in Dynamic Business Intelligent Systems [J]. Journal of Global Optimization. DOI:10.1007/s10898-010-9607-8.
49. Liu. Q. The resolution for rough prepositional logic with lower and upper approximate operators [J]. Lecture Notes in Computer Science,1999,1711:p352-356.
50. Liu. Y., Xu. C., Li. X., Pan. Y. A parallel approximate rule extracting algorithm based on the improved discernibility matrix [J]. Lecture Notes in Artificial Intelligence,2004, 3066:p498-503.
51. Machina. J. "Expected Utility" theory without the independence axiom [J]. Econometrica, 1982(50):p277-323.
52. Mi. J., Wu. W., Zhang. W. Approaches to knowledge reduction based on variable precision rough sets model [J]. Information Sciences,2004,159(3):p255-272.
53. Miyamoto. J., Wakker. P. Multiattribute utility theory expected utility foundations [J]. Operations Research,1996,44(22):p313-326.
54. Morsi. N., Yakout. M. Axiomatics for fuzzy rough sets [J]. Fuzzy Sets and Systems,1998, 100,p327-342.
55. Orlowska. E. A Logic Indiscernibility Relation [J]. Lecture Notes in Computer Science, 1985,208, p117-186.
56. Orlowska. E. Logical aspects of learning concept [J]. International Journal of Approximate reasoning,1988,2:p349-364.
57. Parson. S., Kubat. M., Dohnal. M. A rough set approach to reasoning under uncertainty [J]. Journal of Experimental & Theoretical Artificial Intelligence,1995,7:p175-193.
58. Pawlak. Z. Rough sets [J]. International Journal of Computer Information Sciences,1982, 11(5):p342-356.
59. Pawlak. Z. Rough logic [J]. Bull. Polish Acad. Aci. Tech,1987,35(5-6), p253-258.
60. Pawlak. Z., Wong. S.K.M., Ziarko. W. Rough sets:probabilistic versus determintic approach [J]. International Journal of Man-machine Studies,1988,29:p81-95.
61. Pawlak. Z. Rough set theory and its application to data analysis [J]. Cybernetics and Systems,1998,29:p661-688.
62. Polkowaki. L., Skowron. A. Rough Sets:a perspective rough set in knowledge discovery [M]. Physica-Verlag, Heidellberg,1998.
63. Ramanna. S. Approximation methods in a software quality measurement framework [C]. In:Proceeding of IEEE Conference on Electrical and Computer Engineering,2002,1: p566-571.
64. Radzikowska. A., Kerre. E. A comparative study of fuzzy rough sets [J]. Fuzzy Sets and Systems.2002,126:p137-156.
65. Rommelfanger. H. A PC-supported procedure for solving multicriteria linear programming problems with fuzzy data [J], Interactive fuzzy optimization and mathematical programming,1991, p154-167.
66. Shan. N., Ziarko. W. Data-based acquisition and incremental modification of classification rules [J]. Computational Intelligence,1995,11(2):p357-370.
67. Shusaku. T., Hiroshi. T. Incremental learning of probabilistic rules from clinical database based on rough set theory [J]. Journal of AMIA,1997,4:p198-202.
68. Slezak. D., Ziarko. W. The investigation of the Bayesian rough set model [J]. International Journal of Approximate Reasoning,2005,40:p81-91.
69. Slezak. D. Rough sets and bayes factor. LNCS Transactions on Rough Sets III, LNCS 3400,2005:p202-209.
70. Slowinski. R. Rough sets with strict and weak indiscernibility relations [C]. In: Proceeding of IEEE International Conference on Fuzzy Systems,1992, p695-702.
71. Slowinski. R., Vanderpooten. D. A generalized definition of rough approximations based on similarity [J]. IEEE Transactions on Knowledge and Data Engineering,2000,12: p331-336.
72. Stefanowski. J., Tsoukias. A. incomplete information tables and rough classification [J]. Computational Intelligence,2001,17:p545-566.
73. Stefanowski. J., Tsoukias. A. On the extension of rough sets under incomplete information [J]. Lecture Notes in Artificial Intelligence,1999,1711:p73-81.
74. Su. C., Hsu. J. Precision parameter in the variable precision rough sets model:an application [J]. The International Journal of Management Science,2006,34(2):p149-157.
75. Tong. L. An. L. Incremental learning of decision rules based on rough set theory [C]. In: Proceedings of the World Congress on Intelligent Control and Automation (WCICA), 2002, p420-425.
76. Thomas. B., White C. The dynamic shortest path problem with anticipation [J]. European Journal of Operational Research,2007,176:p836-854.
77. Thomas. D., Jean Y. Dynamic decision making without expected utility:An operational approach [J]. European Journal of Operational Research,2006,169 (1):p 226-246.
78. Tsumoto. S. Accuracy and coverage in rough set rule induction [C]. In:Proceeding of RSCTC 2002, LNAI 2475,2002:p373-380.
79. Tsumoto. S. Mining diagnostic rules from clinical databases using rough sets and medical diagnostic model [J]. Information Sciences,2004; 162(2):p65-80.
80. Tversky. D., Kahneman. A. Prospect theory:an analysis of decision under risk [J]. Econometrica,1979(47):p263-292.
81. Von-Neumann. J., Morgenstern. O. Theory of Games and Economic Behavior [M]. Princeton University,1953.
82. Walczak. B. A rough set theory [J]. Chemometrics and Intelligent Laboratory System, 1999,47:p1-16.
83. Wei. J. Rough set based decision tree, intelligent control and automation [C], In: Proceedings of the 4th World Congress on Control and Automation,2002(1):p426-431.
84. Wong. S.K.M., Ziarko. W., Pawlak. Z. Algorithm for inductive learning [J]. Bulletin of the Polish Academy of Sciences, Technical Sciences,1986,34, p271-276.
85. Wong. S.K.M., Ziarko. W. Comparison of the probabilistic approximate classification and the fuzzy set model [J]. Fuzzy Sets and Systems,1987,21:p357-362.
86. Xie. G., Zhang. J. Variable precision rough set for group decision-making:an application [J]. International Journal of Approximate Reasoning,2008(2):p.331-343.
87. Yao. Y.Y., Wong. S.K.M.:A decision theoretic framework for approximating concepts [J]. International Journal of Man-machine Studies,1992,37(6):p793-809.
88. Yao. Y.Y. Two views of the theory of rough sets in finite universes [J]. International Journal of Approximate Reasoning,1996,15:p291-317.
89. Yao. Y.Y., Li. X. Comparison of rough-set and interval-set models for uncertain reasoning [J]. Fundamenta Informaticae,1996,27 (2-3):p289-298.
90. Yao. Y.Y. Relational interpretations of neighborhood operators and rough set approximation operators [J]. Information Sciences,1998,111(1-4):p239-259.
91. Yao. Y.Y. Probabilistic approaches to rough sets [J]. Expert Systems,2003,20(5): p287-297.
92. Yao. Y.Y. A partition model of granular computing [J]. Transactions on Rough Sets I: 2004,p232-253.
93. Yao. Y.Y. Decision-theoretic rough set models [J]. Lecture Notes in Computer Science, 2007,4481:p1-12.
94. Yang. T. Multiple attribute decision-making methods for the dynamic operator allocation problem [J]. Mathematics and Computers in Simulation,2007,73:p285-299.
95. Yeung. D., Chen. D., Tsang. E., Lee. J., Wang. X. On the generalization of fuzzy rough sets [J]. IEEE Transactions on Fuzzy systems,2005,13(3):p343-361.
96. Yuan. J., Zhang. W. Attribute Reductions in Fuzzy Inconsistent Information Systems [J]. Systems Engineering-theory and Practice,2004,24(5):p116-120.
97. Zadeh. L. The role of fuzzy logic in the management of uncertainty in expert systems [J]. Fuzzy Set and Systems,1983,11(1):p199-227.
98. Zheng. Z., Wang. G. RRIA:A rough set and rule tree based incremental knowledge acquisition algorithm [J]. Fundamenta Informaticae,2004,59(2-3):p299-313.
99. Ziarko. W. Variable precision rough set model [J]. Journal of Computer and System Sciences,1993,46(1):p39-59.
1OO.Ziarko. W. Applying knowledge discovery to predict water-supply consumption [J]. IEEE Expert,1997(4):p72-78.
101.安利平,吴育华,仝凌云.增量式获取规则的粗糙集方法[J].南开大学学报,2003(6)：p98-103.
102.安利平,吴育华,仝凌云.一种基于粗糙集理论的规则获取算法[J].管理科学学报,2001(5)：p75-78.
103.安利平,陈增强,袁著祉.多准则分级决策的扩展粗糙集方法[J].系统工程学报,2004(6)：p9-15.
104.安利平.基于粗集理论的多属性决策分析[M].北京：科学出版社,2008.
105.白南生.中国的城市化[J].管理世界.2003(11)：p78-86.
106.包振强,李长仪,周鑫.基于知识的动态调度决策机制研究[J].中国机械工程,2006(7上)：p1366-1370.
107.崔玉泉,史开泉.粗集的动态特性分析及应用[J].中国管理科学,2003(6)：p66-70.
108.陈珽.决策分析[M].北京：科学出版社,1997.
109.程玉胜,张佑生,胡学钢.基于变精度粗集模型的变精度值自主式获取方法[J].系统仿真学报,2007(11)：p2555-2558.
110.戴文战.一种动态多目标决策模型及其应用[J],控制与决策,2000(2),p197-200.
111.顾婧,周宗放.基于可变精度粗糙集的新兴技术企业信用风险识别[J].管理工程学报,2010(1)：p74-80.
112.管卫华,林振山,陆玉麟.改革开放以来中国城市化水平发展的区域差异研究[J].中国软科学,2008(9)：p74-81.
113.郭亚中,左洪福,王华伟.基于粗糙集的民航飞机故障诊断规则获取方法[J].系统工程理论与实践.2006(11)：p141-146.
114.郭耀煌等.格序决策理论[M].成都：西南交通大学出版社,2002.
115.胡建龙,岳晓冬,李德玉.一种新的规则获取增量式算法[J].山西大学学报(自然科学版).2006,29(2)：p139-141.
116.何亚群,胡寿松.基于粗糙集的空军航材供应点的偏好选址[J].系统工程理论与实践.2003(7)：p96-100.
117.何亚群,胡寿松.不完全信息的多属性粗糙决策分析方法[J].系统工程学报.2004(2)：p9-12.
118.何亚群,李继军,胡寿松.基于模糊相容关系下的相容模糊粗糙集[J].系统工程学报.2006(3)：p 553-556.
119.胡培.决策偏好的相关理论与方法研究[D].西南交通大学博士学位论文,1999.
120.胡寿松,何亚群.粗糙决策理论与应用[M].北京：北京航空航天大学出版社,2006.
121.黄定轩,武振业.基于属性重要性的多属性客观权重分配方法[J].系统工程理论方法应用.2004(3),p203-207.
122.季晓岚,李天瑞,邹维丽,陈红梅.优势关系下属性值粗化细化时近似集分析[J].计算机工程.2010(12),p33-35.
123.蒋朝哲,高晓琴,涂瑞.基于DNA计算机理的粗集属性约简算法构想[J].计算机科学.2005,32(8A)：p48-50.
124.蒋朝哲.粗糙集理论在多属性决策中的应用研究[D].西南交通大学博士研究生学位论文,2006.
125.蒋朝哲.粗集多属性决策理论与方法[M].成都：西南交通大学出版社,2007.
126.菅利荣,达庆利,陈伟达.基于变精度粗糙集的分层知识粒度[J].管理工程学报,2004(2)：p63-66.
127.菅利荣,刘思峰,方志耕等.基于优势粗糙集的教学研究型大学学科建设绩效评价[J].管理工程学报,2007(3)：p136-140.
128.菅利荣.面向不确定性决策问题的杂合粗糙集方法与应用[M].北京：科学出版社,2008.
129.梅虎,朱金福,汪侠.旅客航班选择模型研究：变精度粗集方法[J].管理评论.2007(3)：p27-32.
130.米据生,吴伟志,张文修.基于变精度粗糙集理论的知识约简方法[J].系统工程理论与实践.2004,24(1)：p76-82.
131.李大营,许伟,陈荣秋.基于粗糙集和小波神经网络模型的房地产价格走势预测研究[J].管理评论.2009(11)：p20-24.
132.李红启,刘凯.基于Rough set理论的铁路运量预测[J].铁路学报.2004(3)：p1-7。
133.李天瑞.基于粗糙集的知识动态更新中若干关键问题研究[J].学术动态,2008(1)：p39-40.
134.刘开第,庞彦军,王义闹.粗集中规则提取的一种增量式算法[J].河北建筑科技学 院学报(自然科学版).2001,18(3)：p66-70.
135.刘清Rough集及Rough推理[M].北京：科学出版社.2001.
136.刘清,黄兆华,刘少辉等.带Rough算子的决策规则及数据挖掘中的软计算[J].计算机研究与发展.1999,36(7)：p800-804.
137.刘树安,杜红涛,王晓玲.粗糙集理论及其应用发展[J].系统工程理论与实践,2001(10)：p77-81.
138.刘树林.多属性决策理论方法与应用研究[D].北京航空航天大学博士学位论文,1997.
139.刘伟斌,李天瑞,邹维丽,胡成祥.特性关系粗糙集下属性值粗化细化时近似集增量更新方法研究[J].计算机科学.2010(6)：p248-251.
140.刘学生.基于粗集的不确定多属性决策排序法的研究[D].大连理工大学博士学位论文,2009.
141.刘云忠,宣慧玉,林国玺.粗糙集理论在我国税收预测中的应用[J].系统工程理论与实践.2004(10)：p99-104.
142.蒙祖强,蔡自兴.个性化决策规则的发现：一种基于Rough Set的方法[J].控制与决策.2004(9)：p994-998.
143.苗夺谦,胡桂荣.知识约简的一种启发式算法[J].计算机研究与发展.1999(6)：p42-45.
144.苗夺谦,王国胤,刘清等.粒计算：过去、现在与展望[M].科学出版社,2007。
145.饶从军,肖新平.风险型动态混合多属性决策的灰矩阵关联度法[J].系统工程与电子技术.2006(9)：1353-1357.
146.史开泉,崔玉泉.S-粗集和它的一般结构[J].山东大学学报.2002,37(6)：p471-474.
147.史开泉,崔玉泉.S-粗集与它的分解还原[J].系统工程与电子技术.2005,27(4)：p644-651.
148.史开泉.函数S-粗集.山东大学学报[J].2005,40(1),p1-10.
149.史开泉.S-粗集与粗决策[M].北京：科学出版社,2005.
150.史开泉.函数S-粗集与系统规律挖掘[M].北京：科学出版社,2007.
151.苏雪串.影响我国城市化进程的因素分析[J],中央财经大学学报,2005(2)：p57-60.
152.孙晓琳,姚波,孙晓燕.知识分布和决策权匹配性的动态分析[J].中国统计2003(12)：p27-29.
153.王国胤Rough集理论与知识获取[M].西安：西安交通大学出版社,2001.
154.王国胤Rough集理论在不完备信息系统中的扩充[J].计算机研究与发展,2002(10),p1238-1243.
155.王国胤,于洪,杨大春.基于条件信息熵的决策表约简[J].计算机学报.2002(7)：p88-95.
156.王国胤,张清华,胡军.粒计算研究综述[J].智能系统学报,2007(6)：p8-26.
157.王国胤,姚一豫,于洪.粗糙集理论与应用研究综述[J].计算机学报,2009(7)：p1229-1246.
158.王珏,苗夺谦,周育健.关于Rough Set理论与应用的综述[J].模式识别与人工智能.1994,9(4)：p337-344.
159.王坚强.一类动态多指标决策问题的灰色关联分析方法[J].中国工业大学学报.1999(5)：p548-550.
160.吴艳霞,张道宏.城市发展水平的综合评价及实证分析[J],经济与管理研究,2005(8)：p66-69.
161.肖智,叶世杰,短期电力负荷预测的粗糙集方法[J].系统工程学报,2009(2)：p17-23.
162.徐玖平,吴巍.多属性决策的理论和方法[M].北京：清华大学出版社,2006.
163.徐扬.1*-模理论及格值对策理论的研究[D].西南交通大学博士学位论文,1995.
164.徐泽水.不确定多属性决策方法及应用[M].北京：清华大学出版社,2006.
165.杨善林,刘业政,李亚飞.基于Rough Sets理论的证据获取与合成方法[J].管理科学学报,2005(5)：p73-79.
166.杨祖快,刘鼎臣.基于马尔柯夫决策过程动态WTA最优化模型分析[J].火力与指挥控制.2003(10)：p25-27.
167.叶军,王磊.一种基于粗糙集和层次分析法的综合评价方法研究[J].计算机应用研究,2010(7)：p92-94.
168.於东军,王士同.一种增量式规则提取算法[J].小型微型计算机系统,2004(1)：p79-81.
169.于洪,杨大春,吴中福.基于Rough set理论的增量式规则获取算法[J].小型微型计算机系统.2005,26(1)：p36-41.
170.岳超源.决策理论与方法[M].北京：科学出版社.2003.
171.张金隆,丛国栋,周智皎.一种基于粗糙集和灰聚类理论的IT项目评标决策模型[J].管理评论.2005(10)：p31-35+66.
172.张梅,李怀祖.国际竞争力因素分析的Rough集方法[J].当代经济科学,2003(1),p86-96.
173.张玲,张钹.问题求解理论及应用：商空间粒度计算理论及应用(第2版)[M],北京：清华大学出版社,2007.
174.张文修,吴伟志,梁吉业等.粗糙集理论与方法[M].北京：科学出版社.2001.
175.张文修,仇国芳.基于粗糙集理论的不确定性决策[M].北京：清华大学出版社2005.
176.张燕平,罗斌,姚一豫等.商空间与粒计算—结构化问题求解理论与方法[M],科学出版社,2010.
177.曾黄麟.粗集理论及其应用[M].重庆：重庆大学出版社.1998.
178.钟波,肖智.一种基于粗糙集理论的组合预测方法[J].统计研究.2002(11)：p37-39.
179.钟波,周家启,肖智.基于粗糙集与神经网络的电力负荷新型预测模型[J].系统工程理论与实践.2004(6)：p114-120.
180.左孝凌.离散数学[M].上海：上海科学技术文献出版社.1982.
2. Alam. S., Ghosh. Shrabonti. Rank by AHP:a Rough approach [C]. In:Proceeding of ISCF, 2002,p185-190.
3. Armstrong. R., Cook. W., Seiford. L. Priority ranking and consensus formation:The case of ties [J]. Management Science,1982,28(6):p 638-645.
4. Arrow. K. Alternative Approaches to the Theory of Choice in Risk-Taking Situations [J]. Econometrica,1951(19):p404-437.
5. Bang. W, Zeungnam. B. New incremental learning algorithm in the framework of rough set theory [J]. International Journal of Fuzzy Systems.1999,1(1):p25-36.
6. Becker. J., Sarin. R. Lottery dependent utility [J]. Management Science,1987(33): p1367-1382.
7. Beynon. M. Reducts within the variable precision rough sets model:a further investigation [J]. European Journal of Operational Research.2001,134(3):p592-605.
8. Blaszczynski. J., Greco S., Slowinski R. et. al:On variable consistency dominance-based rough set approaches [C]. In:Proceeding of RSCTC 2006, LNAI 4259,2006, p191-202.
9. Blaszczynski. J., Greco S., Slowinski. R. et. al:Monotonic Variable Consistency Rough Set Approaches [J], International Journal of Approximate Reasoning,2009,50(7), p979-999.
10. Bodjanova. S. Approximation of fuzzy concepts in decision making [J]. Fuzzy Sets and Systems,1997,85:p23-29.
11. Bonikowski. Z, Bryniarski. E., Wybraniec. U. Extensions and intentions in the rough set theory [J]. Information Science,1998,107:p149-167.
12. Bryniarski. E. Some aspects of decision making under uncertainty [J]. Journal of Statistical Planning and Inference,2006,137(8):p2613-2632.
13. Chan. C. A rough set approach to attribute generalization in data mining [J]. Information Sciences,1998,107:p177-194.
14. Chen. H., Li. T., Qiao. S., Ruan. D. A Rough Set Based Dynamic Maintenance Approach for Approximations in Coarsening and Refining Attribute Values [J]. International Journal of Intelligent System,2010, p1005-1026.
15. Chen. S., Fu. G. Combining fuzzy iteration model with dynamic programming to solve multiobjective multistage decision making problems [J]. Fuzzy Sets and Systems, 2005(152):p499-512.
16. Chew. S., MacCrimmon. K. "Alpha-nu Choice Theory:A generalization of expected utility theory" [J]. Working Paper, No.669, University of British Columbia, Vancouver, 1979.
17. Dimitras. A. Business failure prediction using rough sets [J]. European Journal of Operational Research,1999 (114):p263-280.
18. Dubois. D., Prade. H. Rough fuzzy sets and fuzzy rough sets [J]. Journal of General Systems,1990,17(2-3):p191-209.
19. Duntsch. I, Gediga G. Uncertainty measures of rough set prediction [J]. Journal of Artificial Intelligence,1998,106 (1):p77-107.
20. Dyer. J., Fishburn. P., Wallenius. J., et. al. Multiple criteria decision making, Multi-attribute utility theory:the next ten years [J]. Management Science,1992,38(5): p645-653.
21. Edwards. W. The theory of decision-making [J]. Psychological Bulletin,1954,51: p380-417.
22. Edwards. W. Behavioral decision theory [J]. Annual Review of Psychology,1961,12: p473-498.
23. Fishbern. P. Dominance in SSB utility theory [J]. Journal of Economic Theory,1984(34): p130-148.
24. Fishburn. P., Lavalle. I. Multiattribute expected utility without the archimedean axiom [J]. Journal of Mathematical Psychology,1992,36(4):p573-591.
25. Graham. I., Jones. P., Expert systems:knowledge, unicertainty and decision [M]. London, New York,1980.
26. Greco. S., Matarazzo. B., Slowinski. R. Rough set approach to multi-attribute choice and ranking problems [C]. In:Proceedings of the 12th International Conference on Multiple Criteria Decision Making, Hagen, Springer, Berlin,1997, p318-329.
27. Greco. S., Matarazzo. B., Slowinski. R. Rough approximation of a preference relation by dominance relations [J]. European Journal of Operational Research,1999,117:p 63-83.
28. Greco. S., Matarazzo. B., Slowinski. R., Stefanowski. J.:Variable consistency model of dominance-based rough sets approach [C]. In:Proceeding of RSCTC2000, LNAI2005, 2001,p170-181.
29. Greco. S. Rough set theory for multicriterial decision analysis [J], European Journal of Operational Research,2001,129:p1-47.
30. Grzymala-Busse. J. LERS-a system for learning from examples based on rough sets [M]. In:Intelligent Decision Support:Handbook of Applications and Advances of the Rough Sets Theory, Kluwer Academic Publishers, Boston,1992:p3-18.
31. Grzymala-Busse. J. A new version of the rule induction system LERS [J]. Fundamenta Informaticae,1997,31:p37-39.
32. Grzymala-Busse. J., Siddhaye S. Rough set approaches to rule induction from incomplete data [C]. In:Proceeding of International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems,2004, p923-930.
33. Grzymala-Busse. J. Characteristic relations for incomplete data:A generalization of the indiscernibility relation [J]. Transactions on Rough Sets Ⅳ,2005, p58-68.
34. Guo. S., Wang. Z., Wu. Z., Yan. H. A novel dynamic incremental rules extraction algorithm based on rough set theory [C]. In:Proc Proceedings of the Fourth International Conference on Machine Learning and Cybernetics,2005, p1902-1907.
35. Guan. Y., Wang. H. Set-valued information systems [J]. Information Science,2006,176: p2507-2525.
36. Han. J., Kamber. M. Data Mining:Concepts and techniques (2nd Edition) [M], Morgan Kaufmann,2006, USA.
37. Inuiguchi M., Yoshioka.Y.:Variable-precision dominance-based rough set approach [C]. In:Proceeding of RSCTC 2006, LNAI 4259,2006, p203-212.
38. Jia. J., Dyer. J., Butler. J. Measures of Perceived Risk [J]. Management Science,1999, 45(4):p519-532.
39. Kaliszewski. I. Dynamic parametric bounds on efficient outcomes in interactive multiple criteria decision making problems [J], European Journal of Operational Research,2003, 147:p94-107.
40. Katzberg. J., Ziarko. W. Variable precision extension of rough sets [J]. Fundamenta Informaticae,1996,27(2-3):155-168.
41. Kazimierz. Z. Rough approximation of a preference relation by a multi-attribute stochastic dominance for determinist and stochastic evaluation problems [J]. European Journal of Operational Research,2001,130:p305-314.
42. Kryszkiewicz. M. Rough set approach to incomplete information system [J]. Information Sciences,1998,112:p39-49.
43. Li. L., Lai K. Fuzzy dynamic programming approach to hybrid multi-objective multistage decision making problems [J]. Fuzzy Sets and Systems,2001,17:p13-25.
44. Li. T., Ruan. D, Greet. W. et al. A rough set based characteristic relation approach for dynamic attribute generalization in data mining [J]. Knowledge-Based Systems,2007, 20(2):p485-494.
45. Lin. T. and Liu, Q. Rough approximate operators:Axiomatic rough set theory [M]. In: Rough sets, Fuzzy sets and Knowledge Discover. Springer-Verlag, London,1994, p256-260.
46. Lin. T. and Liu, Q. First-order Rough Logic I:approximate reasoning via rough sets [J]. Fundaments Informaticae,1996,27 (2-3):p137-144.
47. Liu. D., Li. T., Ruan. D., Zou. W. An incremental approach for inducing knowledge from dynamic information systems [J]. Fundamenta Informaticae,2009,94 (2):p245-260.
48. Liu. D., Li. T., Ruan. D., Zhang. J. Incremental Learning Optimization on Knowledge Discovery in Dynamic Business Intelligent Systems [J]. Journal of Global Optimization. DOI:10.1007/s10898-010-9607-8.
49. Liu. Q. The resolution for rough prepositional logic with lower and upper approximate operators [J]. Lecture Notes in Computer Science,1999,1711:p352-356.
50. Liu. Y., Xu. C., Li. X., Pan. Y. A parallel approximate rule extracting algorithm based on the improved discernibility matrix [J]. Lecture Notes in Artificial Intelligence,2004, 3066:p498-503.
51. Machina. J. "Expected Utility" theory without the independence axiom [J]. Econometrica, 1982(50):p277-323.
52. Mi. J., Wu. W., Zhang. W. Approaches to knowledge reduction based on variable precision rough sets model [J]. Information Sciences,2004,159(3):p255-272.
53. Miyamoto. J., Wakker. P. Multiattribute utility theory expected utility foundations [J]. Operations Research,1996,44(22):p313-326.
54. Morsi. N., Yakout. M. Axiomatics for fuzzy rough sets [J]. Fuzzy Sets and Systems,1998, 100,p327-342.
55. Orlowska. E. A Logic Indiscernibility Relation [J]. Lecture Notes in Computer Science, 1985,208, p117-186.
56. Orlowska. E. Logical aspects of learning concept [J]. International Journal of Approximate reasoning,1988,2:p349-364.
57. Parson. S., Kubat. M., Dohnal. M. A rough set approach to reasoning under uncertainty [J]. Journal of Experimental & Theoretical Artificial Intelligence,1995,7:p175-193.
58. Pawlak. Z. Rough sets [J]. International Journal of Computer Information Sciences,1982, 11(5):p342-356.
59. Pawlak. Z. Rough logic [J]. Bull. Polish Acad. Aci. Tech,1987,35(5-6), p253-258.
60. Pawlak. Z., Wong. S.K.M., Ziarko. W. Rough sets:probabilistic versus determintic approach [J]. International Journal of Man-machine Studies,1988,29:p81-95.
61. Pawlak. Z. Rough set theory and its application to data analysis [J]. Cybernetics and Systems,1998,29:p661-688.
62. Polkowaki. L., Skowron. A. Rough Sets:a perspective rough set in knowledge discovery [M]. Physica-Verlag, Heidellberg,1998.
63. Ramanna. S. Approximation methods in a software quality measurement framework [C]. In:Proceeding of IEEE Conference on Electrical and Computer Engineering,2002,1: p566-571.
64. Radzikowska. A., Kerre. E. A comparative study of fuzzy rough sets [J]. Fuzzy Sets and Systems.2002,126:p137-156.
65. Rommelfanger. H. A PC-supported procedure for solving multicriteria linear programming problems with fuzzy data [J], Interactive fuzzy optimization and mathematical programming,1991, p154-167.
66. Shan. N., Ziarko. W. Data-based acquisition and incremental modification of classification rules [J]. Computational Intelligence,1995,11(2):p357-370.
67. Shusaku. T., Hiroshi. T. Incremental learning of probabilistic rules from clinical database based on rough set theory [J]. Journal of AMIA,1997,4:p198-202.
68. Slezak. D., Ziarko. W. The investigation of the Bayesian rough set model [J]. International Journal of Approximate Reasoning,2005,40:p81-91.
69. Slezak. D. Rough sets and bayes factor. LNCS Transactions on Rough Sets III, LNCS 3400,2005:p202-209.
70. Slowinski. R. Rough sets with strict and weak indiscernibility relations [C]. In: Proceeding of IEEE International Conference on Fuzzy Systems,1992, p695-702.
71. Slowinski. R., Vanderpooten. D. A generalized definition of rough approximations based on similarity [J]. IEEE Transactions on Knowledge and Data Engineering,2000,12: p331-336.
72. Stefanowski. J., Tsoukias. A. incomplete information tables and rough classification [J]. Computational Intelligence,2001,17:p545-566.
73. Stefanowski. J., Tsoukias. A. On the extension of rough sets under incomplete information [J]. Lecture Notes in Artificial Intelligence,1999,1711:p73-81.
74. Su. C., Hsu. J. Precision parameter in the variable precision rough sets model:an application [J]. The International Journal of Management Science,2006,34(2):p149-157.
75. Tong. L. An. L. Incremental learning of decision rules based on rough set theory [C]. In: Proceedings of the World Congress on Intelligent Control and Automation (WCICA), 2002, p420-425.
76. Thomas. B., White C. The dynamic shortest path problem with anticipation [J]. European Journal of Operational Research,2007,176:p836-854.
77. Thomas. D., Jean Y. Dynamic decision making without expected utility:An operational approach [J]. European Journal of Operational Research,2006,169 (1):p 226-246.
78. Tsumoto. S. Accuracy and coverage in rough set rule induction [C]. In:Proceeding of RSCTC 2002, LNAI 2475,2002:p373-380.
79. Tsumoto. S. Mining diagnostic rules from clinical databases using rough sets and medical diagnostic model [J]. Information Sciences,2004; 162(2):p65-80.
80. Tversky. D., Kahneman. A. Prospect theory:an analysis of decision under risk [J]. Econometrica,1979(47):p263-292.
81. Von-Neumann. J., Morgenstern. O. Theory of Games and Economic Behavior [M]. Princeton University,1953.
82. Walczak. B. A rough set theory [J]. Chemometrics and Intelligent Laboratory System, 1999,47:p1-16.
83. Wei. J. Rough set based decision tree, intelligent control and automation [C], In: Proceedings of the 4th World Congress on Control and Automation,2002(1):p426-431.
84. Wong. S.K.M., Ziarko. W., Pawlak. Z. Algorithm for inductive learning [J]. Bulletin of the Polish Academy of Sciences, Technical Sciences,1986,34, p271-276.
85. Wong. S.K.M., Ziarko. W. Comparison of the probabilistic approximate classification and the fuzzy set model [J]. Fuzzy Sets and Systems,1987,21:p357-362.
86. Xie. G., Zhang. J. Variable precision rough set for group decision-making:an application [J]. International Journal of Approximate Reasoning,2008(2):p.331-343.
87. Yao. Y.Y., Wong. S.K.M.:A decision theoretic framework for approximating concepts [J]. International Journal of Man-machine Studies,1992,37(6):p793-809.
88. Yao. Y.Y. Two views of the theory of rough sets in finite universes [J]. International Journal of Approximate Reasoning,1996,15:p291-317.
89. Yao. Y.Y., Li. X. Comparison of rough-set and interval-set models for uncertain reasoning [J]. Fundamenta Informaticae,1996,27 (2-3):p289-298.
90. Yao. Y.Y. Relational interpretations of neighborhood operators and rough set approximation operators [J]. Information Sciences,1998,111(1-4):p239-259.
91. Yao. Y.Y. Probabilistic approaches to rough sets [J]. Expert Systems,2003,20(5): p287-297.
92. Yao. Y.Y. A partition model of granular computing [J]. Transactions on Rough Sets I: 2004,p232-253.
93. Yao. Y.Y. Decision-theoretic rough set models [J]. Lecture Notes in Computer Science, 2007,4481:p1-12.
94. Yang. T. Multiple attribute decision-making methods for the dynamic operator allocation problem [J]. Mathematics and Computers in Simulation,2007,73:p285-299.
95. Yeung. D., Chen. D., Tsang. E., Lee. J., Wang. X. On the generalization of fuzzy rough sets [J]. IEEE Transactions on Fuzzy systems,2005,13(3):p343-361.
96. Yuan. J., Zhang. W. Attribute Reductions in Fuzzy Inconsistent Information Systems [J]. Systems Engineering-theory and Practice,2004,24(5):p116-120.
97. Zadeh. L. The role of fuzzy logic in the management of uncertainty in expert systems [J]. Fuzzy Set and Systems,1983,11(1):p199-227.
98. Zheng. Z., Wang. G. RRIA:A rough set and rule tree based incremental knowledge acquisition algorithm [J]. Fundamenta Informaticae,2004,59(2-3):p299-313.
99. Ziarko. W. Variable precision rough set model [J]. Journal of Computer and System Sciences,1993,46(1):p39-59.
1OO.Ziarko. W. Applying knowledge discovery to predict water-supply consumption [J]. IEEE Expert,1997(4):p72-78.
101.安利平,吴育华,仝凌云.增量式获取规则的粗糙集方法[J].南开大学学报,2003(6)：p98-103.
102.安利平,吴育华,仝凌云.一种基于粗糙集理论的规则获取算法[J].管理科学学报,2001(5)：p75-78.
103.安利平,陈增强,袁著祉.多准则分级决策的扩展粗糙集方法[J].系统工程学报,2004(6)：p9-15.
104.安利平.基于粗集理论的多属性决策分析[M].北京：科学出版社,2008.
105.白南生.中国的城市化[J].管理世界.2003(11)：p78-86.
106.包振强,李长仪,周鑫.基于知识的动态调度决策机制研究[J].中国机械工程,2006(7上)：p1366-1370.
107.崔玉泉,史开泉.粗集的动态特性分析及应用[J].中国管理科学,2003(6)：p66-70.
108.陈珽.决策分析[M].北京：科学出版社,1997.
109.程玉胜,张佑生,胡学钢.基于变精度粗集模型的变精度值自主式获取方法[J].系统仿真学报,2007(11)：p2555-2558.
110.戴文战.一种动态多目标决策模型及其应用[J],控制与决策,2000(2),p197-200.
111.顾婧,周宗放.基于可变精度粗糙集的新兴技术企业信用风险识别[J].管理工程学报,2010(1)：p74-80.
112.管卫华,林振山,陆玉麟.改革开放以来中国城市化水平发展的区域差异研究[J].中国软科学,2008(9)：p74-81.
113.郭亚中,左洪福,王华伟.基于粗糙集的民航飞机故障诊断规则获取方法[J].系统工程理论与实践.2006(11)：p141-146.
114.郭耀煌等.格序决策理论[M].成都：西南交通大学出版社,2002.
115.胡建龙,岳晓冬,李德玉.一种新的规则获取增量式算法[J].山西大学学报(自然科学版).2006,29(2)：p139-141.
116.何亚群,胡寿松.基于粗糙集的空军航材供应点的偏好选址[J].系统工程理论与实践.2003(7)：p96-100.
117.何亚群,胡寿松.不完全信息的多属性粗糙决策分析方法[J].系统工程学报.2004(2)：p9-12.
118.何亚群,李继军,胡寿松.基于模糊相容关系下的相容模糊粗糙集[J].系统工程学报.2006(3)：p 553-556.
119.胡培.决策偏好的相关理论与方法研究[D].西南交通大学博士学位论文,1999.
120.胡寿松,何亚群.粗糙决策理论与应用[M].北京：北京航空航天大学出版社,2006.
121.黄定轩,武振业.基于属性重要性的多属性客观权重分配方法[J].系统工程理论方法应用.2004(3),p203-207.
122.季晓岚,李天瑞,邹维丽,陈红梅.优势关系下属性值粗化细化时近似集分析[J].计算机工程.2010(12),p33-35.
123.蒋朝哲,高晓琴,涂瑞.基于DNA计算机理的粗集属性约简算法构想[J].计算机科学.2005,32(8A)：p48-50.
124.蒋朝哲.粗糙集理论在多属性决策中的应用研究[D].西南交通大学博士研究生学位论文,2006.
125.蒋朝哲.粗集多属性决策理论与方法[M].成都：西南交通大学出版社,2007.
126.菅利荣,达庆利,陈伟达.基于变精度粗糙集的分层知识粒度[J].管理工程学报,2004(2)：p63-66.
127.菅利荣,刘思峰,方志耕等.基于优势粗糙集的教学研究型大学学科建设绩效评价[J].管理工程学报,2007(3)：p136-140.
128.菅利荣.面向不确定性决策问题的杂合粗糙集方法与应用[M].北京：科学出版社,2008.
129.梅虎,朱金福,汪侠.旅客航班选择模型研究：变精度粗集方法[J].管理评论.2007(3)：p27-32.
130.米据生,吴伟志,张文修.基于变精度粗糙集理论的知识约简方法[J].系统工程理论与实践.2004,24(1)：p76-82.
131.李大营,许伟,陈荣秋.基于粗糙集和小波神经网络模型的房地产价格走势预测研究[J].管理评论.2009(11)：p20-24.
132.李红启,刘凯.基于Rough set理论的铁路运量预测[J].铁路学报.2004(3)：p1-7。
133.李天瑞.基于粗糙集的知识动态更新中若干关键问题研究[J].学术动态,2008(1)：p39-40.
134.刘开第,庞彦军,王义闹.粗集中规则提取的一种增量式算法[J].河北建筑科技学 院学报(自然科学版).2001,18(3)：p66-70.
135.刘清Rough集及Rough推理[M].北京：科学出版社.2001.
136.刘清,黄兆华,刘少辉等.带Rough算子的决策规则及数据挖掘中的软计算[J].计算机研究与发展.1999,36(7)：p800-804.
137.刘树安,杜红涛,王晓玲.粗糙集理论及其应用发展[J].系统工程理论与实践,2001(10)：p77-81.
138.刘树林.多属性决策理论方法与应用研究[D].北京航空航天大学博士学位论文,1997.
139.刘伟斌,李天瑞,邹维丽,胡成祥.特性关系粗糙集下属性值粗化细化时近似集增量更新方法研究[J].计算机科学.2010(6)：p248-251.
140.刘学生.基于粗集的不确定多属性决策排序法的研究[D].大连理工大学博士学位论文,2009.
141.刘云忠,宣慧玉,林国玺.粗糙集理论在我国税收预测中的应用[J].系统工程理论与实践.2004(10)：p99-104.
142.蒙祖强,蔡自兴.个性化决策规则的发现：一种基于Rough Set的方法[J].控制与决策.2004(9)：p994-998.
143.苗夺谦,胡桂荣.知识约简的一种启发式算法[J].计算机研究与发展.1999(6)：p42-45.
144.苗夺谦,王国胤,刘清等.粒计算：过去、现在与展望[M].科学出版社,2007。
145.饶从军,肖新平.风险型动态混合多属性决策的灰矩阵关联度法[J].系统工程与电子技术.2006(9)：1353-1357.
146.史开泉,崔玉泉.S-粗集和它的一般结构[J].山东大学学报.2002,37(6)：p471-474.
147.史开泉,崔玉泉.S-粗集与它的分解还原[J].系统工程与电子技术.2005,27(4)：p644-651.
148.史开泉.函数S-粗集.山东大学学报[J].2005,40(1),p1-10.
149.史开泉.S-粗集与粗决策[M].北京：科学出版社,2005.
150.史开泉.函数S-粗集与系统规律挖掘[M].北京：科学出版社,2007.
151.苏雪串.影响我国城市化进程的因素分析[J],中央财经大学学报,2005(2)：p57-60.
152.孙晓琳,姚波,孙晓燕.知识分布和决策权匹配性的动态分析[J].中国统计2003(12)：p27-29.
153.王国胤Rough集理论与知识获取[M].西安：西安交通大学出版社,2001.
154.王国胤Rough集理论在不完备信息系统中的扩充[J].计算机研究与发展,2002(10),p1238-1243.
155.王国胤,于洪,杨大春.基于条件信息熵的决策表约简[J].计算机学报.2002(7)：p88-95.
156.王国胤,张清华,胡军.粒计算研究综述[J].智能系统学报,2007(6)：p8-26.
157.王国胤,姚一豫,于洪.粗糙集理论与应用研究综述[J].计算机学报,2009(7)：p1229-1246.
158.王珏,苗夺谦,周育健.关于Rough Set理论与应用的综述[J].模式识别与人工智能.1994,9(4)：p337-344.
159.王坚强.一类动态多指标决策问题的灰色关联分析方法[J].中国工业大学学报.1999(5)：p548-550.
160.吴艳霞,张道宏.城市发展水平的综合评价及实证分析[J],经济与管理研究,2005(8)：p66-69.
161.肖智,叶世杰,短期电力负荷预测的粗糙集方法[J].系统工程学报,2009(2)：p17-23.
162.徐玖平,吴巍.多属性决策的理论和方法[M].北京：清华大学出版社,2006.
163.徐扬.1*-模理论及格值对策理论的研究[D].西南交通大学博士学位论文,1995.
164.徐泽水.不确定多属性决策方法及应用[M].北京：清华大学出版社,2006.
165.杨善林,刘业政,李亚飞.基于Rough Sets理论的证据获取与合成方法[J].管理科学学报,2005(5)：p73-79.
166.杨祖快,刘鼎臣.基于马尔柯夫决策过程动态WTA最优化模型分析[J].火力与指挥控制.2003(10)：p25-27.
167.叶军,王磊.一种基于粗糙集和层次分析法的综合评价方法研究[J].计算机应用研究,2010(7)：p92-94.
168.於东军,王士同.一种增量式规则提取算法[J].小型微型计算机系统,2004(1)：p79-81.
169.于洪,杨大春,吴中福.基于Rough set理论的增量式规则获取算法[J].小型微型计算机系统.2005,26(1)：p36-41.
170.岳超源.决策理论与方法[M].北京：科学出版社.2003.
171.张金隆,丛国栋,周智皎.一种基于粗糙集和灰聚类理论的IT项目评标决策模型[J].管理评论.2005(10)：p31-35+66.
172.张梅,李怀祖.国际竞争力因素分析的Rough集方法[J].当代经济科学,2003(1),p86-96.
173.张玲,张钹.问题求解理论及应用：商空间粒度计算理论及应用(第2版)[M],北京：清华大学出版社,2007.
174.张文修,吴伟志,梁吉业等.粗糙集理论与方法[M].北京：科学出版社.2001.
175.张文修,仇国芳.基于粗糙集理论的不确定性决策[M].北京：清华大学出版社2005.
176.张燕平,罗斌,姚一豫等.商空间与粒计算—结构化问题求解理论与方法[M],科学出版社,2010.
177.曾黄麟.粗集理论及其应用[M].重庆：重庆大学出版社.1998.
178.钟波,肖智.一种基于粗糙集理论的组合预测方法[J].统计研究.2002(11)：p37-39.
179.钟波,周家启,肖智.基于粗糙集与神经网络的电力负荷新型预测模型[J].系统工程理论与实践.2004(6)：p114-120.
180.左孝凌.离散数学[M].上海：上海科学技术文献出版社.1982.