粗糙集理论及其在高等教育评估中的应用研究
|
摘要
数量方法是高等教育评估方法中最为重要的方法之一。由于评估环境的复杂性与动态性,使得评估主体所面临的数据具有多样性与不确定性,传统的评估技术已不能适应高等教育评估的现实要求。本文研究了基于粗糙集理论的高等教育评估方法,针对不完备信息条件下的基于拓展优势关系的粗糙决策分析方法,提出了限制优势关系、确定优势关系、限制相似优势关系等新的拓展优势关系,对原有的拓展优势关系进行了有效的改进和推广;针对属性约简结果的不唯一性,以条件信息熵为工具,给出了属性约简的择优方法;研究了属性约简的算法,提出了一种基于论域划分类归并的粗糙集属性约简新的算法;在以上研究的基础上,对江苏省高职高专人才培养水平评估工作作了实证分析。本文的主要创新点有:
(1)在不完备信息决策系统中,提出了限制优势关系以及确定优势关系的概念,更合理地处理了属性未知值与属性已知值的比较问题。对限制优势关系以及确定优势关系分别给出了求得相应粗糙集属性约简的具体方法。对比结果表明,使用新的拓展优势关系可以获得可信度更高的粗糙决策规则。
(2)将确定优势关系的概念推广到粗糙模糊集中,并在粗糙模糊集中提出了限制相似优势关系。在确定优势关系及限制相似优势关系粗糙模糊集的基础上,提出了属性约简的概念,给出了求得这些约简的具体操作方法。使用基于确定优势关系以及限制相似优势关系的粗糙模糊决策分析方法,可以获取不完备模糊目标信息系统的“至少”、“至多”决策规则。
(3)为排除决策过程中噪声因素的干扰,基于确定优势关系提出了变精度粗糙决策分析方法。以从多个粗糙集属性约简中选择最优的约简为目的,分析了作为度量工具的现有条件信息熵在应用过程中的缺陷。借鉴变精度粗糙集理论的思想,在对阈值参数进行二次选择的基础上,提出一种新的条件信息熵。基于新的条件信息熵设计了一种变精度优势粗糙集属性约简的择优算法,克服了现有条件信息熵的不足。
(4)针对现有求属性约简算法的不足,以论域的不同划分与条件属性的不同子集确定的等价关系间的一一对应关系为出发点,研究了粗糙集属性约简与论域划分类归并之间的内在联系,利用粗糙集模型某些属性约简的正区域不变性,提出一种基于论域划分类归并的粗糙集属性约简新算法。
(5)从决策理论的角度出发,在以上粗糙集理论研究基础上,对江苏省高职高专人才培养工作水平评估数据库进行实证分析,揭示了江苏省高职高专人才培养工作水平评估一级指标与院校评估整体结论之间的内在规律,为教育主管部门、评估机构、参评院校提供一些有价值的决策信息及政策建议。
Quantitative method is one of the most important methods of higher edueation evaluation. Because of the complexity and dynamic of the environment, the evaluation subject has to deal with diverse and uncertain data, and the traditional methods could not meet the practical requirements of the higher education evaluation. This paper mainly discusses methods of higher edueation evaluation based on rough set theory. New expansion of dominance relations such as limited dominance relation, definitive dominance relation and limited similarity dominance relation are proposed for multi-attribute decision making problems with preference and incomplete information. For the results of attribute reduction are not unique, a preferred method in which conditional information entropy is used to measure reduction of attributes is proposed. Intrinsic relationship between attributes reduction relation of rough sets and classification of universe is researched and an efficient attribute reduction algorithm of rough sets based on classification merging is given by using an invariance of positive region of some rough sets. Based on the research on rough set theory, the empirical analysis of the talents cultivation level assessment project for higher technical and vocational institution of Jiangsu province are made. The main innovations of the paper are as follows:
(1) In the incomplete information decision-making system, the limited dominance relation and definitive dominance relation proposed are more rationally to deal with the known and unknown attribute values. Based on the limited dominance-based and definitive dominance-based rough sets, two types of knowledge reductions are proposed. Then, the practical approaches to compute the reductions are presented. One can obtain higher quality of decision rules by using the limited dominance-based and definitive dominance-based rough sets model.
(2) The concept of definitive dominance relation and limited similarity dominance relation are proposed in the rough fuzzy set. And based on above new extended dominance relations, two types of knowledge reductions are proposed. Then, the practical approaches to compute the reductions are presented.“At least”and“at most”rules can be obtained by using definitive dominance-based and limited similarity dominance-based rough sets model in the incomplete fuzzy objective information system.
(3) In order to exclude the interference of the noise factor, based on definitive dominance relation, an analysis method of decision-making based on variable precision rough set is proposed. Then, the disadvantages of the current conditional information entropy are analyzed when conditional information entropy is used to measure reduction of attributes of dominance-based rough sets. Variable precision rough set theory is used as a basis for proposing a new condition information, with another parameter choosed. Then, a new algorithm based on new condition information entropy is designed to choose reduction of attributes of Variable precision dominance rough set, which is better than others.
(4) Intrinsic relationship between attributes reduction relation of rough set and classification of universe is researched through one to one mapping relation between classification of universe and equivalence relation determined by subset of attribute set. Using an invariance of positive region of some rough sets, an efficient attribute reduction algorithm of rough set based on classification merging is proposed.
(5) Based on the decision-making theory and the research on rough set theory, the empirical analysis of the talents cultivation level assessment project for higher technical and vocational institution of Jiangsu province are made. The Inherent discipline between the class 1 index and the general evaluation is revealed which can provide some valuable decision-making information and policy recommendations for educational authorities, evaluation agency and assessed institutions.
(1)在不完备信息决策系统中,提出了限制优势关系以及确定优势关系的概念,更合理地处理了属性未知值与属性已知值的比较问题。对限制优势关系以及确定优势关系分别给出了求得相应粗糙集属性约简的具体方法。对比结果表明,使用新的拓展优势关系可以获得可信度更高的粗糙决策规则。
(2)将确定优势关系的概念推广到粗糙模糊集中,并在粗糙模糊集中提出了限制相似优势关系。在确定优势关系及限制相似优势关系粗糙模糊集的基础上,提出了属性约简的概念,给出了求得这些约简的具体操作方法。使用基于确定优势关系以及限制相似优势关系的粗糙模糊决策分析方法,可以获取不完备模糊目标信息系统的“至少”、“至多”决策规则。
(3)为排除决策过程中噪声因素的干扰,基于确定优势关系提出了变精度粗糙决策分析方法。以从多个粗糙集属性约简中选择最优的约简为目的,分析了作为度量工具的现有条件信息熵在应用过程中的缺陷。借鉴变精度粗糙集理论的思想,在对阈值参数进行二次选择的基础上,提出一种新的条件信息熵。基于新的条件信息熵设计了一种变精度优势粗糙集属性约简的择优算法,克服了现有条件信息熵的不足。
(4)针对现有求属性约简算法的不足,以论域的不同划分与条件属性的不同子集确定的等价关系间的一一对应关系为出发点,研究了粗糙集属性约简与论域划分类归并之间的内在联系,利用粗糙集模型某些属性约简的正区域不变性,提出一种基于论域划分类归并的粗糙集属性约简新算法。
(5)从决策理论的角度出发,在以上粗糙集理论研究基础上,对江苏省高职高专人才培养工作水平评估数据库进行实证分析,揭示了江苏省高职高专人才培养工作水平评估一级指标与院校评估整体结论之间的内在规律,为教育主管部门、评估机构、参评院校提供一些有价值的决策信息及政策建议。
Quantitative method is one of the most important methods of higher edueation evaluation. Because of the complexity and dynamic of the environment, the evaluation subject has to deal with diverse and uncertain data, and the traditional methods could not meet the practical requirements of the higher education evaluation. This paper mainly discusses methods of higher edueation evaluation based on rough set theory. New expansion of dominance relations such as limited dominance relation, definitive dominance relation and limited similarity dominance relation are proposed for multi-attribute decision making problems with preference and incomplete information. For the results of attribute reduction are not unique, a preferred method in which conditional information entropy is used to measure reduction of attributes is proposed. Intrinsic relationship between attributes reduction relation of rough sets and classification of universe is researched and an efficient attribute reduction algorithm of rough sets based on classification merging is given by using an invariance of positive region of some rough sets. Based on the research on rough set theory, the empirical analysis of the talents cultivation level assessment project for higher technical and vocational institution of Jiangsu province are made. The main innovations of the paper are as follows:
(1) In the incomplete information decision-making system, the limited dominance relation and definitive dominance relation proposed are more rationally to deal with the known and unknown attribute values. Based on the limited dominance-based and definitive dominance-based rough sets, two types of knowledge reductions are proposed. Then, the practical approaches to compute the reductions are presented. One can obtain higher quality of decision rules by using the limited dominance-based and definitive dominance-based rough sets model.
(2) The concept of definitive dominance relation and limited similarity dominance relation are proposed in the rough fuzzy set. And based on above new extended dominance relations, two types of knowledge reductions are proposed. Then, the practical approaches to compute the reductions are presented.“At least”and“at most”rules can be obtained by using definitive dominance-based and limited similarity dominance-based rough sets model in the incomplete fuzzy objective information system.
(3) In order to exclude the interference of the noise factor, based on definitive dominance relation, an analysis method of decision-making based on variable precision rough set is proposed. Then, the disadvantages of the current conditional information entropy are analyzed when conditional information entropy is used to measure reduction of attributes of dominance-based rough sets. Variable precision rough set theory is used as a basis for proposing a new condition information, with another parameter choosed. Then, a new algorithm based on new condition information entropy is designed to choose reduction of attributes of Variable precision dominance rough set, which is better than others.
(4) Intrinsic relationship between attributes reduction relation of rough set and classification of universe is researched through one to one mapping relation between classification of universe and equivalence relation determined by subset of attribute set. Using an invariance of positive region of some rough sets, an efficient attribute reduction algorithm of rough set based on classification merging is proposed.
(5) Based on the decision-making theory and the research on rough set theory, the empirical analysis of the talents cultivation level assessment project for higher technical and vocational institution of Jiangsu province are made. The Inherent discipline between the class 1 index and the general evaluation is revealed which can provide some valuable decision-making information and policy recommendations for educational authorities, evaluation agency and assessed institutions.
引文
[1]陈玉琨.教育评价学.北京:人民教育出版社, 1999.
[2]张远增.高等教育评价方法研究.上海:复旦大学出版社, 2002.
[3]杨晓江,刘艳萍.中国高教评估的实践与走向.中国高等教育评估, 2005, 3: 14~17.
[4]杨晓江,刘艳萍.高校迎评:什么是你的策略选择.高教发展与评估, 2005, 21(5): 30~36.
[5]杨晓江,吴立平.我国高职高专评估工作亟待改进.高教发展与评估, 2006, 22(5): 48~52.
[6]杨晓江.教育评估机构的理想冲突.辽宁教育研究, 2003, 1: 9~13.
[7]杨晓江.高等教育评估机构的历史使命.中国高等教育评估, 2002, 4: 3~7.
[8]杨晓江.高等教育质量观必须与时俱进.全球教育展望, 2002, 31(1): 23~25.
[9]杨晓江.教育评估的科学性与科学的教育评估.教育研究, 2000, 8: 33~36.
[10]杨晓江.教育评估中介机构五年研究述评.高等教育研究, 1999, 3: 28~32.
[11]刘康宁,董云川.高职高专院校人才培养工作评估指标的聚类分析.高教发展与评估, 2007, 23(2): 76~80.
[12]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.管理工程学报, 2007, 3: 132~136.
[13]孙继红,杨晓江.基于粗糙集的一级学科整体水平评估结果评价.科技进步与对策, 2009, 26(5): 138~141.
[14] Z Pawlak. Rough sets. International Journal of Information&Computer Sciences, 1982,11: 341~356.
[15]王国胤. Rough集理论与知识获取.西安:西安交通大学出版社, 2001: 168~207.
[16]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.
[17] W Ziarko. Variable precision rough set model. Journal of Computer and System Sciences, 1993, 46(1): 39~59.
[18] M Kryszkiewicz. Properties of incomplete information systems in the framework of rough sets.In:Polkowski L.,Skowron A.(Eds.), Rough Sets in Knowledge Discovery. Physica, Heidelberg, 1998: 422~450.
[19] J Stefanowski., A Tsoukias.On the Extension of Rough Sets under Incomplete Information. In: Zhong N, Skowron A, Ohsuga S ed.Proceedings of the 7th International Workshop on NewDirections in Rough Sets, Data Mining,and Granular-Soft Computing,Yamaguchi: Physica-Verlag, 1999: 73~81.
[20] J Stefanowski., A Tsoukias.Valued Tolerance and Decision Rules.In W.Ziarko,Y.Yao (eds.),Rough Sets and Current Trends in Computing,Springer Verlag, LNAI 2005, Berlin, 2001: 212~219.
[21] Y.Y. Yao. Information granulation and rough set approximation. International Journal of Intelligent Systems, 2001, 16 (1): 87~104.
[22] S Greco., B Matarazzo.,Slowinski R.Rough sets theory for multicriteria decision analysis. European Journal of Operational Research, 2001, 129(1): 1~47.
[23] S Greco., B Matarazzo.,Slowinski R., Stefanowski J. Variable consistency model of dominance-based rough setsapproach.Rough Sets and Current Trends in Computing, Lecture Notes in Artificial Intelligence(RSCTC’2000), Berlin: Springer-Verlag, 2001: 170~181.
[24] Z. Pawlak. J.W Grzymala-Busse. Slowinski R. Rough sets.Communication of ACM. 1995, 38(11): 89~95.
[25]杨宁.粗糙集理论及其在投资环境因素分析中的应用研究, [博士学位论文].成都:西南交通大学, 2006.
[26] Golan R.,Ziarko W.. Methodology for Stoek Market Analysis Utilizing RoughSet Theory. NewJersey: Process of IEEE/IAFE Conference on ComPutational Intelligence for Financial Engineering, 1995: 32~40.
[27]高晓康.粗糙集理论研究及其在工程和医学诊断中的应用, [博士学位论文].上海:同济大学, 2007.
[28] Teghe M.. Use of rough sets method to draw Premonitory factors forearth quakes by emphasizing gas geochemistry Intelligent Decision Support-Handbook of applications and Advances of the Rough Sets Theory. Dordrecht: Kluwer Academic Publishers, 1992: 165~179
[29] Deja R.. Confliet Model with negotiations. Institute of Computer Scinece Reports. Warsaw University of Techno1ogy , Warsaw, 1995.
[30]刘泓.基于粗糙集理论的车牌识别系统的研究与实现, [博士学位论文].合肥:合肥工业大学, 2003.
[31]徐立中,王慧斌,杨锦堂.基于粗糙集理论的图像增强方法.仪器仪表学报, 2000, 10(5): 514~516.
[32] Plonka L.M.. A Rule-based stabilization of the inverted pendulum.ComPutational Intelligenee, 1995, 11(2): 348~356.
[33]谭天乐.基于粗糙集的过程建模、控制与故障诊断, [博士学位论文].杭州:浙江大学, 2003.
[34]何亚群,胡寿松.不完全信息的多属性粗糙决策分析方法,系统工程学报, 2004, 19(2): 117~120.
[35]胡明礼,刘思峰.基于有限扩展优势关系的粗糙决策分析方法.系统工程, 2006, 24(4): 106~110.
[36]胡明礼,刘思峰.基于广义扩展优势关系的粗糙决策分析方法,控制与决策, 2007, 22(12): 1347~1351.
[37]黄兵,周献中.不完备信息系统中基于联系度的粗集模型拓展,系统工程理论与实践, 2004, 24(1): 88~92.
[38] D Dubois, H Prade. Rough fuzzy sets and fuzzy rough sets. Information and Computer Science, 1990, 17(2): 191~209.
[39] S. Greco, M. Inuiguchi, R. Slowinski. Fuzzy rough sets and multiple-premise gradual decision rules. International Journal of Approximate Reasoning, 2006, 41(2): 179~211.
[40] J.S. Mi, Y. Leung, W.Z. Wu. An uncertainty measure in partition-based fuzzy rough sets. International Journal of General Systems, 2005, 34(1): 77~90.
[41] J.S. Mi, W.X. Zhang. An axiomatic characterization of a fuzzy generalization of rough sets. Information Sciences, 2004, 160 (1–4): 235~249.
[42] W.Z. Wu, W.X. Zhang, H.Z. Li. Knowledge acquisition in incomplete fuzzy information systems via rough set approach. Expert Systems, 2003, 20 (5): 280~286.
[43] W.Z. Wu, J.S. Mi, W.X. Zhang. Generalized fuzzy rough sets. Information Sciences, 2003, 151(5): 263~282.
[44] W.Z. Wu, W.X. Zhang. Neighborhood operator systems and approximations. Information Sciences, 2002 ,144(1-4): 201~217.
[45] W.Z. Wu, W.X. Zhang. Constructive and axiomatic approaches of fuzzy approximation operators. Information Sciences, 2004, 159 (3-4): 233~254.
[46] A. de Korvin, C. McKeegan, R. Kleyle. Knowledge acquisition using rough sets when membership values are fuzzy sets. Journal of Intelligent and Fuzzy Systems, 1998, 6(2): 237~244.
[47]丛蓉,王秀坤,杨南海,等.融合粗糙集和D-S方法的空中目标类型识别算法.控制与决策, 2008, 23(8):915~918.
[48]胡春海,王晓丽,邹晓红. D-S证据理论和粗集理论在数据融合中的应用.现代雷达, 2004, 26(9): 53~55.
[49]杨海龙,孙健国.粒子群优化的粗糙集-神经网络在航空发动机故障诊断中的应用.航空动力学报, 2009, 24(2): 458~464.
[50]张腾飞,马福民.粗糙-神经网络逆模补偿的发电机励磁复合控制.电机与控制学报, 2009, 13(1): 104~111.
[51]柯孔林,冯宗宪.基于粗糙集和神经网络集成的贷款风险5级分类.控制理论与应用, 2008, 25(4): 759~763.
[52]薛锋,柯孔林.粗糙集-神经网络系统在商业银行贷款五级分类中的应用.系统工程理论与实践, 2008, 1: 40~45.
[53]文振华,左洪福.基于粗糙集-集成神经网络的航空发动机磨损故障诊断方法.中国机械工程, 2007, 18(21): 2580~2584.
[54]张赢,李琛.基于粗糙集理论的神经网络研究及应用.控制与决策, 2007, 22(4): 462~464.
[55]代建华,陈卫东,潘云鹤.基于粗糙集的综合推理模型.浙江大学学报(工学版), 2009, 40(9): 1526~1530.
[56]廖伟志,古天龙,元昌安,等.基于一阶混杂Petri网的柔性制造系统粗糙控制.控制与决策, 2009, 24(3): 365~370.
[57]廖伟志,古天龙.基于一种新型混杂Petri网的混杂系统建模与控制.控制与决策, 2007, 22(4): 366~372.
[58]原菊梅,侯朝桢,高琳,等.粗糙Petri网及其在多状态系统可靠性估计中的应用.兵工学报, 2007, 28(11): 1373~1376.
[59]刘永建,朱剑英,夏洪山,等.飞机复杂系统故障诊断的灰色粗集推理方法.南京航空航天大学学报, 2009, 41(2): 227~231.
[60]胡方,黄建国,褚福照.基于粗糙集的武器系统灰色关联评估模型.兵工学报, 2008, 29(2): 253~256.
[61]齐继阳,竺长安.智能故障诊断粗糙灰色组合模型.哈尔滨工业大学学报, 2007, 39(3): 501~504.
[62]菅利荣.面向不确定性决策的杂合粗糙集方法及其应用.北京:科学出版社, 2008.
[63]戴毓,周德群.基于粗糙集理论的格序选择方法.中国管理科学, 2007, 15(6): 105~110.
[64] S. Greco, B. Matarazzo, R. S?owiński. Dominance-based rough set approach to case-based reasoning. in Modeling decisions for artificial intelligence, Third international conference, Lecture Notes in Computer Science, 2006, 3885: 7~18.
[65] Y. Leung, W.Z. Wu, W.X. Zhang. Knowledge acquisition in incomplete information systems: A rough set approach.European Journal of Operational Research, 2006, 168(1): 164~180.
[66] H.L. Li , M.H. Chen. Induction of multiple criteria optimal classification rules for biological and medical data. Computers in Biology and Medicine, 2008, 38(1): 42~52.
[67] L.X. Shen, H.T. Loh. Applying rough sets to market timing decisions. Decision Support Systems, 2004, 37(4): 583~597.
[68] R.W. Swiniarski, A. Skowron. Rough set method in feature selection and recognition. Pattern Recognition Letter, 2003, 24(6): 833~849.
[69] Eric C.C. Tsang, S.Y. Zhao, D.S. Yeung, and John W.T. Lee. Learning from an incomplete information system with continuous-valued attributes by a rough set technique. in Proceedings of 2005 International Conference on Machine Learning and Cybernetics(ICMLC 2005), Lecture Notes in Artificial Intelligence, 2006, 3930: 568~577.
[70] G. Xie, J. Zhang, K.K. Lai, and L. Yu. Variable precision rough set for group decision-making: an application. International Journal of Approximate Reasoning (2007), doi: 10.1016/j.ijar.2007.04.005.
[71] Z. Pawlak. Rough setstheoretical aspects of reasoning about data. Kluwer Academic Publishers, 1991.
[72] Z. Pawlak. Rough set theory and its applications to data analysis. Cybernetics and Systems, 1998, 29(7): 661~688.
[73] Z. Pawlak. Rough sets and intelligent data analysis. Information Sciences, 2002, 147(1): 1~12.
[74] Z. Pawlak. Some remarks on conflict analysis. European Journal of Operational Research, 2005, 166: 649~654.
[75] Z. Pawlak, A. Skowron. Rudiments of rough sets. Information Sciences, 2007, 177(1): 3~27.
[76] Z. Pawlak , A. Skowron. Rough sets: Some extensions. Information Sciences, 2007, 177(1): 28~40.
[77] Z. Pawlak, A. Skowron. Rough sets and boolean reasoning. Information Sciences, 2007, 177(1): 41~73.
[78] Z. Pawlak, R. Slowinski. Decision analysis using rough sets. International Transactions in Operational Research, 1994, 1(1): 107~114.
[79] S Greco, B. Matarazzo, S?owiński R. Rough approximation by dominance relations. International Journal of Intelligent Systems, 2002, 17(2): 153~171.
[80] T F Fan, D R Liu, Tzeng G H. Rough set-based logics for multicriteria decision analysis. European Journal of Operational Research, 2007, 182(1): 340~355.
[81] X B Yang, J Y Yang, C Wu, et al. Dominance-based rough set approach and knowledge reductions in incomplete ordered information system . Information Sciences, 2008, 178(4): 1219~1234.
[82] M W Shao, W X Zhang. Dominance relation and rules in an incomplete ordered information system [J]. International Journal of Intelligent Systems, 2005, 20(1): 13~27.
[83] J. B?aszczyński, S. Greco, and R. S?owiński. On variable consistency dominance-based rough set approaches. in The fifth international conference on rough sets and current trends in computing (RSCTC 2006), Lecture Notes in Artificial Intelligence, 2006, 4259: 191~202.
[84] J. B?aszczyński, S. Greco, and R. S?owiński. Monotonic variable consistency rough set approaches. in Rough Setsand Knowledge Technology, Second International Conference (RSKT 2007), Lecture Notes in Computer Science, 2007, 4481: 126~133.
[85] J. B?aszczyński, S. Greco, and R. S?owiński. Multi-criteria classification-A new scheme for application of dominance-based decision rules. European Journal of Operational Research, 2007, 181(3): 1030~1044.
[86] K. Dembczyński, R. Pindur, and R. Susmaga. Dominance-based rough set classifier without induction of decision rules. Electronic Notes in Theoretical Computer Science, 2003, 82(4): 84~95.
[87] K. Dembczyński, S. Greco, and R. S?owiński. Second-order rough approximations in multi-criteria classification with imprecise evaluations and assignments. in Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC 2005), Lecture Notes in Artificial Intelligence, 2005, 3641: 54~63.
[88] T. F. Fan, D.R. Liu, and G.H. Tzeng. Rough set-based logics for multicriteria decision analysis. European Journal of Operational Research, 2007, 182(1): 340~355.
[89] S. Greco, Masahiro Inuiguchi, and R. S?owiński. Dominance-based rough set approach using possibility and necessity measures. in RSCTC 2002, Lecture Notes in Artificial Intelligence, 2002, 2475: 85~92.
[90] S. Greco, B. Matarazzo, and R. S?owiński. Handing missing values in rough set analysis of mutiattribute and muti-criteria decision problems. in New directions in rough sets, data mining and granular-soft computing, Lecture Notes in Artificial Intelligence, 1999, 1711: 146~157.
[91] Q.H. Hu and D.R. Yu. Variable precision dominance based rough set model and reduction algorithm for preference-ordered data. in Proceedings of the Third International Conference on Machine Learning and Cybernetics, 2004: 26~29.
[92] J.W. Grzymala-Busse. Characteristic relations for incomplete data: a generalization of the indiscernibility relation. in Proceeding of the Third International Conference on Rough Sets and Current Trends in Computing, Lecture Notes in Artificial Intelligence, 2004, 3066: 244~253.
[93] J.W. Grzymala-Busse. Data with missing attribute values: generalization of indiscernibility relation and rule rnduction. Transactions on Rough Sets I, Lecture Notes in Computer Science, 2004, 3100: 78~95.
[94] M. Kryszkiewicz. Rough set approach to incomplete information systems. Information Sciences, 1998, 112(1): 39~49.
[95] M. Kryszkiewicz. Rules in incomplete information systems. Information Sciences, 1999, 113(3): 271~292.
[96] Y. Leung and D.Y. Li. Maximal consistent block technique for rule acquisition in incomplete information systems. Information Sciences, 2003, 115(7): 85~106.
[97] Y.H. Qian, C.Y. Dang, J.Y. Liang, H.Y. Zhang, and J.M. Ma. On the evaluation of the decision performance of an incomplete decision table. Data & Knowledge Engineering, 2008, 65(3): 373~400.
[98] J. Stefanowski and A. Tsoukiàs. On the extension of rough sets under incomplete information. in Proceeding of RSFDGrC99, Lecture Notes in Artificial Intelligence, 1999, 1711: 73~82.
[99] J. Stefanowski and A. Tsoukiμas. Incomplete information tables and rough classification. Computational Intelligence, 2001, 17(3): 545~566.
[100] G.Y. Wang. Extension of rough set under incomplete information systems. in Proceedings of the 11th IEEE International Conference on Fuzzy Systems, 2002: 1098~1103.
[101] W.Z. Wu. Attribute reduction based on evidence theory in incomplete decision systems. Information Sciences, 2008, 178(5): 1355~1371.
[102] T.P. Hong, L.H. Tseng, S.L. Wang. Learning rules from incomplete training examples by rough sets. Expert Systems with Applications, 2002, 22(4): 285~293.
[103] A. Skowron and C. Rauszer. The discernibility matrices and functions in information systems. in Intelligent Decision Support-Handbook of Applications and Advances of the Rough Sets Theory, 1992: 331~362.
[104] L H Wei, Z M Tang, X B Yang, et al.Dominance-based rough set approach to incomplete fuzzy information system, in: X.H. Hu et al. (Eds.): 2008 IEEE International Conference on Granular Computing, 26-28, August, Hangzhou, 2008: 632~637.
[105] J S Mi, W Z Wu, W X Zhang..Approaches to knowledge reduction based on variable precision rough set model. Information Sciences, 2004, 159(3): 255~272.
[106] Y Zhao, Y Y Yao, F Luo. Data analysis based on discernibility and indiscernibility. Information Sciences, 2007, 177(22): 4959~4976.
[107] X B Yang, J Xie, X N Song, et al. Credible rules in incomplete decision system based on descriptors. Knowledge-Based Systems, 2009, 22(1): 8~17.
[108] M. Inuiguchi and Y. Yoshioka. Several reducts in dominance-based rough set approach, in: V.-N. Huynh et al. (Eds.): Interval/Probabilistic Uncertainty and Non-classical Logics, Advance on Soft Computing, vol.46, Springer-Verlag, Berlin, Heidelberg, 2008: 163~175.
[109]颜锦江,黄兵.不完备信息系统中基于相似度的变精度粗糙集模型.系统工程理论与实践, 2006, 10(10): 68~72.
[110]孙士保,姚磊磊,吴庆涛,等.变精度粗糙集模型及其应用研究.计算机工程与应用, 2009, 45(7): 10~13.
[111] M. Inuiguchi, Y. Yoshioka, Y. Kusunoki, Variable-precision dominance-based rough set approach and attribute reduction, Internat. J. Approx. Reason. (2009), doi: 10.1016/j.ijar.2009.02.003.
[112]庞哈利,高政威,左军伟,等.基于变精度粗糙集的分类决策树构造方法.系统工程与电子技术,2008, 30(11): 2160~2163.
[113]索中英,朱林户,吴华,等.基于变精度粗糙集的航空发动机故障诊断.航空动力学报, 2008, 23(10): 1842~1846.
[114]张永敢,蔡瑞英.基于变精度粗糙集的故障诊断应用研究.计算机工程与设计, 2009, 30(3): 657~659.
[115]黎文航,陈善本,王加友,等.基于变精度粗糙集的脉冲GTAW过程建模方法.焊接学报, 2008, 29(7):57~59.
[116]赵亮,薛质.基于变精度粗糙集的多属性群决策信息系统安全评估.上海交通大学学报, 2009, 43(7): 1161~1166.
[117]冯力娜,高会生,王东蕊.基于变精度粗糙集的光纤通信网风险评估.电力系统通信, 2009, 30(201): 68~71.
[118]倪啸,蔡瑞英.基于变精度粗糙集的信息系统数据挖掘应用.微电子学与计算机, 2009, 26(4): 89~91.
[119] J Y Liang, K S Chin, C Y Dang,et al. A new method for measuring uncertainty and fuzziness in rough set theory[J]. International Journal of General Systems, 2002, 31 (4): 331~342.
[120] J Y Liang, Z Z Shi. The information entropy, rough entropy and knowledge granulation in rough theory. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2004, 12(1): 37~46.
[121] Q H Hu, D R Yu. Entropies of indiscernibility relation and its operations. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2004, 12(5): 575~589.
[122] J Y Liang, Z Z Shi, D Y Li and M. J. Wierman. Information entropy, rough entropy and knowledge granulation in incomplete information systems. International Journal of General Systems, 2006, 35(6): 641 ~654.
[123] Düntsch I, Gediga G. Uncertainty measures of rough seprediction. Artificial Intelligence, 1998, 106: 109~137.
[124]王国胤,于洪,杨大春.基于条件信息熵的决策表约简.计算机学报, 2002, 25(7): 759~766.
[125]刘启和,李凡,闵帆,等.一种基于新的条件信息熵的高效知识约简算法.控制与决策, 2005, 20(8): 878~882.
[126]潘郁,菅利荣,达庆利.多标准决策表中发现概率规则的变精度粗糙集方法.中国管理科学, 2005, 13(1): 95~100.
[127]陈湘晖,朱善君,吉吟东.基于熵和变精度粗糙集的规则不确定性量度.清华大学学报, 2001, 41(3): 109~112.
[128]陶志,许宝,汪定伟,等.基于遗传算法的粗糙集知识约简方法.系统工程, 2003, 21(4): 116~122
[129]刘少辉,盛秋戬,吴斌,等. Rough集高效算法的研究.计算机学报, 2003, 26(5): 524~529.
[130]徐章艳,刘作鹏,杨炳儒,等.一个复杂度为max(O(|C||U|),(|C|2|U/C|))的快速属性约简算法.计算机学报, 2006,29(3): 391~399.
[131] Xiaohua Hu. Knowledge discovery in databases: Aattribute-oriented rough set approach, [博士学位论文]. Saskatchewan Canada: University of Regina, Department of Compute Science, 1995.
[132]王国胤.决策表核属性的计算方法.计算机学报, 2003, 26(5): 611~615.
[133] Q. Hu, Z. Xie, D. Yu, Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation, Pattern Recognition, 2007, 40(12): 3509~3521.
[134]王加阳,罗安,陈松乔.可变精度粗糙集模型区间约简.系统工程学报, 2007, 22(6): 620~626.
[135]蔡娜,张雪峰,王宇彤.变精度粗糙集属性约简的算法.系统工程与电子技术, 2007, 29(12): 2064~2067.
[136]丁守祯,桑琳,朱全英,等.基于信息熵的粗糙集属性约简及其应用.计算机工程与应用2007, 43(35): 245~248.
[137]蔡娜,张雪峰.基于贝叶斯粗糙集模型的属性约简.计算机工程, 2007, 33(24): 46~48.
[138]张旭,郭晨.基于免疫原理的粗糙集属性约简.计算机工程, 2007, 33(23): 51~53.
[139]杨明.决策表中基于条件信息熵的近似约简.电子学报, 2007, 35(11): 2156~2160.
[140]陶志,刘庆拯,李卫民.一种基于改进区分矩阵的属性约简算法.计算机工程与应用, 2007, 43(32): 83~85.
[141]舒芬,王加阳.一种改进的规则分辨矩阵及其属性值约简方法.计算机工程与应用, 2007, 43(32): 77~79.
[142]陶志,刘庆拯,李卫民.基于遗传算法的不完备信息系统属性约简方法.系统工程与电子技术, 2007, 29(9): 1484~1487.
[143]侯利娟,史长琼.基于分明矩阵的属性约简启发式算法.计算机工程与设计, 2007, 28(18): 4466~4468.
[144]杨明.一种基于改进差别矩阵的属性约简增量式更新算法.计算机学报, 2007, 30(5): 815~822.
[145] Q.Hu, D.Yu, Z.Xie. Information-preserving hybrid data reduction based on fuzzy-rough techniques. Pattern Recognition Letters, 2006, 27(5): 414~423.
[146] M.Liu, D.Chen ,C.Wu, H.Li. Reduction method based on a new fuzzy rough set in fuzzy information system and its applications to scheduling problems. Computers and Mathematics with Applications, 2006, 51: 1571~1584.
[147]高学东,丁军.基于简化差别矩阵的属性约简算法.系统工程理论与实践, 2006, 6: 101~107.
[148] D.Y. Li, B. Zhang, Y. Leung. On knowledge reduction in inconsistent decision information systems. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2004, 12(5): 651~672.
[149] Liang J, Xu Z.The algorithm on knowledge reduction in incomplete information systems.Internationnal Journal of Uncertainty Fuzziness and Knowledge-Based Systems, 2002, 10(1): 95~103.
[150] Jue Wang, Ju Wang. Reduction algorithms based on discernibility matrix:The ordered attributions method. Journal of Computer Science and Technology, 2001, 16(6): 489~504.
[151] M. Kryszkiewicz. Comparative study of alternative types of knowledge reduction in inconsistent systems. International Journal of Intelligent Systems, 2001, 16(11): 105~120.
[152] BeynonM.Reductswithin the variable precision rough set model: A further investigation. European Journal of Opera-tion Research, 2001, 134(3): 592~605.
[153]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.系统工程理论与实践, 2004, 24(1): 76~82.
[154]朱平天,李伯葓,邹园.近世代数.北京:科学出版社, 2003.
[155]杨晓江.高等教育评估立法初探.辽宁教育研究, 2007, 11: 28~32.
[156]韩延明.论高校教学评估制度的构建.教育发展研究, 2006, 10A: 4~8.
[157]刘理.高校教学评估的教育价值取向分析.教育研究, 2006, 318(7): 30~34.
[158]刘智运.论中国高等教育评估制度的完善与发展——以本科教学工作水平评估为例.清华大学教育研究,2007, 28(3): 40~48.
[159]郭桂英.略论高等教育评估市场及其培育.中国高教研究, 2007, 10:16~18.
[160]刘献君.以质量为核心的教学评估体系构建——兼论我国本科教学工作水平评估.高等教育研究, 2007, 28(7): 37~43.
[161]储祖旺,戴冬秀,宋华民.高校教学评估与建立ISO9000族质量管理体系的比较研究.中国高等教育, 2006, 17: 50~51.
[162]曹勇安,高玉贵,李国文.高职院校引入ISO9000族标准的可行性辨析.教育发展研究, 2005, 11B: 95~99.
[163]孔祥沛.论我国高等教育评估面临的十大挑战.辽宁教育研究, 2007, 2: 97~99.
[164]欧阳胜,朱永东.对高校教育质量评估的反思.江西教育科研, 2007,12: 59~60.
[165]罗建国.我国高等教育政府评估的困境与对策分析.高等教育研究, 2007, 28(3): 28~34.
[166]高耀明,张萍,陈慧,等.本科教学工作水平评估对高校教学工作影响的调查研究.高等教育研究, 2006, 27(11): 85~90.
[167]袁银南,王贵成,韩广才,等.试论本科教学工作水平评估的复杂性和有效性.中国高教研究, 2006, 10: 50~51.
[168]欧阳康.对本科教学工作水平评估若干关系的思考.中国高等教育, 2006, 17: 47~49.
[169]刘振天.对本科教学评估中几个深层次问题的认识.中国高等教育, 2007, 20: 31~32.
[170]邱平.关于高职教学评估的思考.高教探索, 2007, 4: 112~113.
[171]教育部.高职高专院校人才培养工作水平评估工作指南.教高司函[2004] 16号.
[172]江苏省教育评估院.江苏省高职高专评估手册, 2006.
[2]张远增.高等教育评价方法研究.上海:复旦大学出版社, 2002.
[3]杨晓江,刘艳萍.中国高教评估的实践与走向.中国高等教育评估, 2005, 3: 14~17.
[4]杨晓江,刘艳萍.高校迎评:什么是你的策略选择.高教发展与评估, 2005, 21(5): 30~36.
[5]杨晓江,吴立平.我国高职高专评估工作亟待改进.高教发展与评估, 2006, 22(5): 48~52.
[6]杨晓江.教育评估机构的理想冲突.辽宁教育研究, 2003, 1: 9~13.
[7]杨晓江.高等教育评估机构的历史使命.中国高等教育评估, 2002, 4: 3~7.
[8]杨晓江.高等教育质量观必须与时俱进.全球教育展望, 2002, 31(1): 23~25.
[9]杨晓江.教育评估的科学性与科学的教育评估.教育研究, 2000, 8: 33~36.
[10]杨晓江.教育评估中介机构五年研究述评.高等教育研究, 1999, 3: 28~32.
[11]刘康宁,董云川.高职高专院校人才培养工作评估指标的聚类分析.高教发展与评估, 2007, 23(2): 76~80.
[12]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.管理工程学报, 2007, 3: 132~136.
[13]孙继红,杨晓江.基于粗糙集的一级学科整体水平评估结果评价.科技进步与对策, 2009, 26(5): 138~141.
[14] Z Pawlak. Rough sets. International Journal of Information&Computer Sciences, 1982,11: 341~356.
[15]王国胤. Rough集理论与知识获取.西安:西安交通大学出版社, 2001: 168~207.
[16]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.
[17] W Ziarko. Variable precision rough set model. Journal of Computer and System Sciences, 1993, 46(1): 39~59.
[18] M Kryszkiewicz. Properties of incomplete information systems in the framework of rough sets.In:Polkowski L.,Skowron A.(Eds.), Rough Sets in Knowledge Discovery. Physica, Heidelberg, 1998: 422~450.
[19] J Stefanowski., A Tsoukias.On the Extension of Rough Sets under Incomplete Information. In: Zhong N, Skowron A, Ohsuga S ed.Proceedings of the 7th International Workshop on NewDirections in Rough Sets, Data Mining,and Granular-Soft Computing,Yamaguchi: Physica-Verlag, 1999: 73~81.
[20] J Stefanowski., A Tsoukias.Valued Tolerance and Decision Rules.In W.Ziarko,Y.Yao (eds.),Rough Sets and Current Trends in Computing,Springer Verlag, LNAI 2005, Berlin, 2001: 212~219.
[21] Y.Y. Yao. Information granulation and rough set approximation. International Journal of Intelligent Systems, 2001, 16 (1): 87~104.
[22] S Greco., B Matarazzo.,Slowinski R.Rough sets theory for multicriteria decision analysis. European Journal of Operational Research, 2001, 129(1): 1~47.
[23] S Greco., B Matarazzo.,Slowinski R., Stefanowski J. Variable consistency model of dominance-based rough setsapproach.Rough Sets and Current Trends in Computing, Lecture Notes in Artificial Intelligence(RSCTC’2000), Berlin: Springer-Verlag, 2001: 170~181.
[24] Z. Pawlak. J.W Grzymala-Busse. Slowinski R. Rough sets.Communication of ACM. 1995, 38(11): 89~95.
[25]杨宁.粗糙集理论及其在投资环境因素分析中的应用研究, [博士学位论文].成都:西南交通大学, 2006.
[26] Golan R.,Ziarko W.. Methodology for Stoek Market Analysis Utilizing RoughSet Theory. NewJersey: Process of IEEE/IAFE Conference on ComPutational Intelligence for Financial Engineering, 1995: 32~40.
[27]高晓康.粗糙集理论研究及其在工程和医学诊断中的应用, [博士学位论文].上海:同济大学, 2007.
[28] Teghe M.. Use of rough sets method to draw Premonitory factors forearth quakes by emphasizing gas geochemistry Intelligent Decision Support-Handbook of applications and Advances of the Rough Sets Theory. Dordrecht: Kluwer Academic Publishers, 1992: 165~179
[29] Deja R.. Confliet Model with negotiations. Institute of Computer Scinece Reports. Warsaw University of Techno1ogy , Warsaw, 1995.
[30]刘泓.基于粗糙集理论的车牌识别系统的研究与实现, [博士学位论文].合肥:合肥工业大学, 2003.
[31]徐立中,王慧斌,杨锦堂.基于粗糙集理论的图像增强方法.仪器仪表学报, 2000, 10(5): 514~516.
[32] Plonka L.M.. A Rule-based stabilization of the inverted pendulum.ComPutational Intelligenee, 1995, 11(2): 348~356.
[33]谭天乐.基于粗糙集的过程建模、控制与故障诊断, [博士学位论文].杭州:浙江大学, 2003.
[34]何亚群,胡寿松.不完全信息的多属性粗糙决策分析方法,系统工程学报, 2004, 19(2): 117~120.
[35]胡明礼,刘思峰.基于有限扩展优势关系的粗糙决策分析方法.系统工程, 2006, 24(4): 106~110.
[36]胡明礼,刘思峰.基于广义扩展优势关系的粗糙决策分析方法,控制与决策, 2007, 22(12): 1347~1351.
[37]黄兵,周献中.不完备信息系统中基于联系度的粗集模型拓展,系统工程理论与实践, 2004, 24(1): 88~92.
[38] D Dubois, H Prade. Rough fuzzy sets and fuzzy rough sets. Information and Computer Science, 1990, 17(2): 191~209.
[39] S. Greco, M. Inuiguchi, R. Slowinski. Fuzzy rough sets and multiple-premise gradual decision rules. International Journal of Approximate Reasoning, 2006, 41(2): 179~211.
[40] J.S. Mi, Y. Leung, W.Z. Wu. An uncertainty measure in partition-based fuzzy rough sets. International Journal of General Systems, 2005, 34(1): 77~90.
[41] J.S. Mi, W.X. Zhang. An axiomatic characterization of a fuzzy generalization of rough sets. Information Sciences, 2004, 160 (1–4): 235~249.
[42] W.Z. Wu, W.X. Zhang, H.Z. Li. Knowledge acquisition in incomplete fuzzy information systems via rough set approach. Expert Systems, 2003, 20 (5): 280~286.
[43] W.Z. Wu, J.S. Mi, W.X. Zhang. Generalized fuzzy rough sets. Information Sciences, 2003, 151(5): 263~282.
[44] W.Z. Wu, W.X. Zhang. Neighborhood operator systems and approximations. Information Sciences, 2002 ,144(1-4): 201~217.
[45] W.Z. Wu, W.X. Zhang. Constructive and axiomatic approaches of fuzzy approximation operators. Information Sciences, 2004, 159 (3-4): 233~254.
[46] A. de Korvin, C. McKeegan, R. Kleyle. Knowledge acquisition using rough sets when membership values are fuzzy sets. Journal of Intelligent and Fuzzy Systems, 1998, 6(2): 237~244.
[47]丛蓉,王秀坤,杨南海,等.融合粗糙集和D-S方法的空中目标类型识别算法.控制与决策, 2008, 23(8):915~918.
[48]胡春海,王晓丽,邹晓红. D-S证据理论和粗集理论在数据融合中的应用.现代雷达, 2004, 26(9): 53~55.
[49]杨海龙,孙健国.粒子群优化的粗糙集-神经网络在航空发动机故障诊断中的应用.航空动力学报, 2009, 24(2): 458~464.
[50]张腾飞,马福民.粗糙-神经网络逆模补偿的发电机励磁复合控制.电机与控制学报, 2009, 13(1): 104~111.
[51]柯孔林,冯宗宪.基于粗糙集和神经网络集成的贷款风险5级分类.控制理论与应用, 2008, 25(4): 759~763.
[52]薛锋,柯孔林.粗糙集-神经网络系统在商业银行贷款五级分类中的应用.系统工程理论与实践, 2008, 1: 40~45.
[53]文振华,左洪福.基于粗糙集-集成神经网络的航空发动机磨损故障诊断方法.中国机械工程, 2007, 18(21): 2580~2584.
[54]张赢,李琛.基于粗糙集理论的神经网络研究及应用.控制与决策, 2007, 22(4): 462~464.
[55]代建华,陈卫东,潘云鹤.基于粗糙集的综合推理模型.浙江大学学报(工学版), 2009, 40(9): 1526~1530.
[56]廖伟志,古天龙,元昌安,等.基于一阶混杂Petri网的柔性制造系统粗糙控制.控制与决策, 2009, 24(3): 365~370.
[57]廖伟志,古天龙.基于一种新型混杂Petri网的混杂系统建模与控制.控制与决策, 2007, 22(4): 366~372.
[58]原菊梅,侯朝桢,高琳,等.粗糙Petri网及其在多状态系统可靠性估计中的应用.兵工学报, 2007, 28(11): 1373~1376.
[59]刘永建,朱剑英,夏洪山,等.飞机复杂系统故障诊断的灰色粗集推理方法.南京航空航天大学学报, 2009, 41(2): 227~231.
[60]胡方,黄建国,褚福照.基于粗糙集的武器系统灰色关联评估模型.兵工学报, 2008, 29(2): 253~256.
[61]齐继阳,竺长安.智能故障诊断粗糙灰色组合模型.哈尔滨工业大学学报, 2007, 39(3): 501~504.
[62]菅利荣.面向不确定性决策的杂合粗糙集方法及其应用.北京:科学出版社, 2008.
[63]戴毓,周德群.基于粗糙集理论的格序选择方法.中国管理科学, 2007, 15(6): 105~110.
[64] S. Greco, B. Matarazzo, R. S?owiński. Dominance-based rough set approach to case-based reasoning. in Modeling decisions for artificial intelligence, Third international conference, Lecture Notes in Computer Science, 2006, 3885: 7~18.
[65] Y. Leung, W.Z. Wu, W.X. Zhang. Knowledge acquisition in incomplete information systems: A rough set approach.European Journal of Operational Research, 2006, 168(1): 164~180.
[66] H.L. Li , M.H. Chen. Induction of multiple criteria optimal classification rules for biological and medical data. Computers in Biology and Medicine, 2008, 38(1): 42~52.
[67] L.X. Shen, H.T. Loh. Applying rough sets to market timing decisions. Decision Support Systems, 2004, 37(4): 583~597.
[68] R.W. Swiniarski, A. Skowron. Rough set method in feature selection and recognition. Pattern Recognition Letter, 2003, 24(6): 833~849.
[69] Eric C.C. Tsang, S.Y. Zhao, D.S. Yeung, and John W.T. Lee. Learning from an incomplete information system with continuous-valued attributes by a rough set technique. in Proceedings of 2005 International Conference on Machine Learning and Cybernetics(ICMLC 2005), Lecture Notes in Artificial Intelligence, 2006, 3930: 568~577.
[70] G. Xie, J. Zhang, K.K. Lai, and L. Yu. Variable precision rough set for group decision-making: an application. International Journal of Approximate Reasoning (2007), doi: 10.1016/j.ijar.2007.04.005.
[71] Z. Pawlak. Rough setstheoretical aspects of reasoning about data. Kluwer Academic Publishers, 1991.
[72] Z. Pawlak. Rough set theory and its applications to data analysis. Cybernetics and Systems, 1998, 29(7): 661~688.
[73] Z. Pawlak. Rough sets and intelligent data analysis. Information Sciences, 2002, 147(1): 1~12.
[74] Z. Pawlak. Some remarks on conflict analysis. European Journal of Operational Research, 2005, 166: 649~654.
[75] Z. Pawlak, A. Skowron. Rudiments of rough sets. Information Sciences, 2007, 177(1): 3~27.
[76] Z. Pawlak , A. Skowron. Rough sets: Some extensions. Information Sciences, 2007, 177(1): 28~40.
[77] Z. Pawlak, A. Skowron. Rough sets and boolean reasoning. Information Sciences, 2007, 177(1): 41~73.
[78] Z. Pawlak, R. Slowinski. Decision analysis using rough sets. International Transactions in Operational Research, 1994, 1(1): 107~114.
[79] S Greco, B. Matarazzo, S?owiński R. Rough approximation by dominance relations. International Journal of Intelligent Systems, 2002, 17(2): 153~171.
[80] T F Fan, D R Liu, Tzeng G H. Rough set-based logics for multicriteria decision analysis. European Journal of Operational Research, 2007, 182(1): 340~355.
[81] X B Yang, J Y Yang, C Wu, et al. Dominance-based rough set approach and knowledge reductions in incomplete ordered information system . Information Sciences, 2008, 178(4): 1219~1234.
[82] M W Shao, W X Zhang. Dominance relation and rules in an incomplete ordered information system [J]. International Journal of Intelligent Systems, 2005, 20(1): 13~27.
[83] J. B?aszczyński, S. Greco, and R. S?owiński. On variable consistency dominance-based rough set approaches. in The fifth international conference on rough sets and current trends in computing (RSCTC 2006), Lecture Notes in Artificial Intelligence, 2006, 4259: 191~202.
[84] J. B?aszczyński, S. Greco, and R. S?owiński. Monotonic variable consistency rough set approaches. in Rough Setsand Knowledge Technology, Second International Conference (RSKT 2007), Lecture Notes in Computer Science, 2007, 4481: 126~133.
[85] J. B?aszczyński, S. Greco, and R. S?owiński. Multi-criteria classification-A new scheme for application of dominance-based decision rules. European Journal of Operational Research, 2007, 181(3): 1030~1044.
[86] K. Dembczyński, R. Pindur, and R. Susmaga. Dominance-based rough set classifier without induction of decision rules. Electronic Notes in Theoretical Computer Science, 2003, 82(4): 84~95.
[87] K. Dembczyński, S. Greco, and R. S?owiński. Second-order rough approximations in multi-criteria classification with imprecise evaluations and assignments. in Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC 2005), Lecture Notes in Artificial Intelligence, 2005, 3641: 54~63.
[88] T. F. Fan, D.R. Liu, and G.H. Tzeng. Rough set-based logics for multicriteria decision analysis. European Journal of Operational Research, 2007, 182(1): 340~355.
[89] S. Greco, Masahiro Inuiguchi, and R. S?owiński. Dominance-based rough set approach using possibility and necessity measures. in RSCTC 2002, Lecture Notes in Artificial Intelligence, 2002, 2475: 85~92.
[90] S. Greco, B. Matarazzo, and R. S?owiński. Handing missing values in rough set analysis of mutiattribute and muti-criteria decision problems. in New directions in rough sets, data mining and granular-soft computing, Lecture Notes in Artificial Intelligence, 1999, 1711: 146~157.
[91] Q.H. Hu and D.R. Yu. Variable precision dominance based rough set model and reduction algorithm for preference-ordered data. in Proceedings of the Third International Conference on Machine Learning and Cybernetics, 2004: 26~29.
[92] J.W. Grzymala-Busse. Characteristic relations for incomplete data: a generalization of the indiscernibility relation. in Proceeding of the Third International Conference on Rough Sets and Current Trends in Computing, Lecture Notes in Artificial Intelligence, 2004, 3066: 244~253.
[93] J.W. Grzymala-Busse. Data with missing attribute values: generalization of indiscernibility relation and rule rnduction. Transactions on Rough Sets I, Lecture Notes in Computer Science, 2004, 3100: 78~95.
[94] M. Kryszkiewicz. Rough set approach to incomplete information systems. Information Sciences, 1998, 112(1): 39~49.
[95] M. Kryszkiewicz. Rules in incomplete information systems. Information Sciences, 1999, 113(3): 271~292.
[96] Y. Leung and D.Y. Li. Maximal consistent block technique for rule acquisition in incomplete information systems. Information Sciences, 2003, 115(7): 85~106.
[97] Y.H. Qian, C.Y. Dang, J.Y. Liang, H.Y. Zhang, and J.M. Ma. On the evaluation of the decision performance of an incomplete decision table. Data & Knowledge Engineering, 2008, 65(3): 373~400.
[98] J. Stefanowski and A. Tsoukiàs. On the extension of rough sets under incomplete information. in Proceeding of RSFDGrC99, Lecture Notes in Artificial Intelligence, 1999, 1711: 73~82.
[99] J. Stefanowski and A. Tsoukiμas. Incomplete information tables and rough classification. Computational Intelligence, 2001, 17(3): 545~566.
[100] G.Y. Wang. Extension of rough set under incomplete information systems. in Proceedings of the 11th IEEE International Conference on Fuzzy Systems, 2002: 1098~1103.
[101] W.Z. Wu. Attribute reduction based on evidence theory in incomplete decision systems. Information Sciences, 2008, 178(5): 1355~1371.
[102] T.P. Hong, L.H. Tseng, S.L. Wang. Learning rules from incomplete training examples by rough sets. Expert Systems with Applications, 2002, 22(4): 285~293.
[103] A. Skowron and C. Rauszer. The discernibility matrices and functions in information systems. in Intelligent Decision Support-Handbook of Applications and Advances of the Rough Sets Theory, 1992: 331~362.
[104] L H Wei, Z M Tang, X B Yang, et al.Dominance-based rough set approach to incomplete fuzzy information system, in: X.H. Hu et al. (Eds.): 2008 IEEE International Conference on Granular Computing, 26-28, August, Hangzhou, 2008: 632~637.
[105] J S Mi, W Z Wu, W X Zhang..Approaches to knowledge reduction based on variable precision rough set model. Information Sciences, 2004, 159(3): 255~272.
[106] Y Zhao, Y Y Yao, F Luo. Data analysis based on discernibility and indiscernibility. Information Sciences, 2007, 177(22): 4959~4976.
[107] X B Yang, J Xie, X N Song, et al. Credible rules in incomplete decision system based on descriptors. Knowledge-Based Systems, 2009, 22(1): 8~17.
[108] M. Inuiguchi and Y. Yoshioka. Several reducts in dominance-based rough set approach, in: V.-N. Huynh et al. (Eds.): Interval/Probabilistic Uncertainty and Non-classical Logics, Advance on Soft Computing, vol.46, Springer-Verlag, Berlin, Heidelberg, 2008: 163~175.
[109]颜锦江,黄兵.不完备信息系统中基于相似度的变精度粗糙集模型.系统工程理论与实践, 2006, 10(10): 68~72.
[110]孙士保,姚磊磊,吴庆涛,等.变精度粗糙集模型及其应用研究.计算机工程与应用, 2009, 45(7): 10~13.
[111] M. Inuiguchi, Y. Yoshioka, Y. Kusunoki, Variable-precision dominance-based rough set approach and attribute reduction, Internat. J. Approx. Reason. (2009), doi: 10.1016/j.ijar.2009.02.003.
[112]庞哈利,高政威,左军伟,等.基于变精度粗糙集的分类决策树构造方法.系统工程与电子技术,2008, 30(11): 2160~2163.
[113]索中英,朱林户,吴华,等.基于变精度粗糙集的航空发动机故障诊断.航空动力学报, 2008, 23(10): 1842~1846.
[114]张永敢,蔡瑞英.基于变精度粗糙集的故障诊断应用研究.计算机工程与设计, 2009, 30(3): 657~659.
[115]黎文航,陈善本,王加友,等.基于变精度粗糙集的脉冲GTAW过程建模方法.焊接学报, 2008, 29(7):57~59.
[116]赵亮,薛质.基于变精度粗糙集的多属性群决策信息系统安全评估.上海交通大学学报, 2009, 43(7): 1161~1166.
[117]冯力娜,高会生,王东蕊.基于变精度粗糙集的光纤通信网风险评估.电力系统通信, 2009, 30(201): 68~71.
[118]倪啸,蔡瑞英.基于变精度粗糙集的信息系统数据挖掘应用.微电子学与计算机, 2009, 26(4): 89~91.
[119] J Y Liang, K S Chin, C Y Dang,et al. A new method for measuring uncertainty and fuzziness in rough set theory[J]. International Journal of General Systems, 2002, 31 (4): 331~342.
[120] J Y Liang, Z Z Shi. The information entropy, rough entropy and knowledge granulation in rough theory. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2004, 12(1): 37~46.
[121] Q H Hu, D R Yu. Entropies of indiscernibility relation and its operations. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2004, 12(5): 575~589.
[122] J Y Liang, Z Z Shi, D Y Li and M. J. Wierman. Information entropy, rough entropy and knowledge granulation in incomplete information systems. International Journal of General Systems, 2006, 35(6): 641 ~654.
[123] Düntsch I, Gediga G. Uncertainty measures of rough seprediction. Artificial Intelligence, 1998, 106: 109~137.
[124]王国胤,于洪,杨大春.基于条件信息熵的决策表约简.计算机学报, 2002, 25(7): 759~766.
[125]刘启和,李凡,闵帆,等.一种基于新的条件信息熵的高效知识约简算法.控制与决策, 2005, 20(8): 878~882.
[126]潘郁,菅利荣,达庆利.多标准决策表中发现概率规则的变精度粗糙集方法.中国管理科学, 2005, 13(1): 95~100.
[127]陈湘晖,朱善君,吉吟东.基于熵和变精度粗糙集的规则不确定性量度.清华大学学报, 2001, 41(3): 109~112.
[128]陶志,许宝,汪定伟,等.基于遗传算法的粗糙集知识约简方法.系统工程, 2003, 21(4): 116~122
[129]刘少辉,盛秋戬,吴斌,等. Rough集高效算法的研究.计算机学报, 2003, 26(5): 524~529.
[130]徐章艳,刘作鹏,杨炳儒,等.一个复杂度为max(O(|C||U|),(|C|2|U/C|))的快速属性约简算法.计算机学报, 2006,29(3): 391~399.
[131] Xiaohua Hu. Knowledge discovery in databases: Aattribute-oriented rough set approach, [博士学位论文]. Saskatchewan Canada: University of Regina, Department of Compute Science, 1995.
[132]王国胤.决策表核属性的计算方法.计算机学报, 2003, 26(5): 611~615.
[133] Q. Hu, Z. Xie, D. Yu, Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation, Pattern Recognition, 2007, 40(12): 3509~3521.
[134]王加阳,罗安,陈松乔.可变精度粗糙集模型区间约简.系统工程学报, 2007, 22(6): 620~626.
[135]蔡娜,张雪峰,王宇彤.变精度粗糙集属性约简的算法.系统工程与电子技术, 2007, 29(12): 2064~2067.
[136]丁守祯,桑琳,朱全英,等.基于信息熵的粗糙集属性约简及其应用.计算机工程与应用2007, 43(35): 245~248.
[137]蔡娜,张雪峰.基于贝叶斯粗糙集模型的属性约简.计算机工程, 2007, 33(24): 46~48.
[138]张旭,郭晨.基于免疫原理的粗糙集属性约简.计算机工程, 2007, 33(23): 51~53.
[139]杨明.决策表中基于条件信息熵的近似约简.电子学报, 2007, 35(11): 2156~2160.
[140]陶志,刘庆拯,李卫民.一种基于改进区分矩阵的属性约简算法.计算机工程与应用, 2007, 43(32): 83~85.
[141]舒芬,王加阳.一种改进的规则分辨矩阵及其属性值约简方法.计算机工程与应用, 2007, 43(32): 77~79.
[142]陶志,刘庆拯,李卫民.基于遗传算法的不完备信息系统属性约简方法.系统工程与电子技术, 2007, 29(9): 1484~1487.
[143]侯利娟,史长琼.基于分明矩阵的属性约简启发式算法.计算机工程与设计, 2007, 28(18): 4466~4468.
[144]杨明.一种基于改进差别矩阵的属性约简增量式更新算法.计算机学报, 2007, 30(5): 815~822.
[145] Q.Hu, D.Yu, Z.Xie. Information-preserving hybrid data reduction based on fuzzy-rough techniques. Pattern Recognition Letters, 2006, 27(5): 414~423.
[146] M.Liu, D.Chen ,C.Wu, H.Li. Reduction method based on a new fuzzy rough set in fuzzy information system and its applications to scheduling problems. Computers and Mathematics with Applications, 2006, 51: 1571~1584.
[147]高学东,丁军.基于简化差别矩阵的属性约简算法.系统工程理论与实践, 2006, 6: 101~107.
[148] D.Y. Li, B. Zhang, Y. Leung. On knowledge reduction in inconsistent decision information systems. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2004, 12(5): 651~672.
[149] Liang J, Xu Z.The algorithm on knowledge reduction in incomplete information systems.Internationnal Journal of Uncertainty Fuzziness and Knowledge-Based Systems, 2002, 10(1): 95~103.
[150] Jue Wang, Ju Wang. Reduction algorithms based on discernibility matrix:The ordered attributions method. Journal of Computer Science and Technology, 2001, 16(6): 489~504.
[151] M. Kryszkiewicz. Comparative study of alternative types of knowledge reduction in inconsistent systems. International Journal of Intelligent Systems, 2001, 16(11): 105~120.
[152] BeynonM.Reductswithin the variable precision rough set model: A further investigation. European Journal of Opera-tion Research, 2001, 134(3): 592~605.
[153]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.系统工程理论与实践, 2004, 24(1): 76~82.
[154]朱平天,李伯葓,邹园.近世代数.北京:科学出版社, 2003.
[155]杨晓江.高等教育评估立法初探.辽宁教育研究, 2007, 11: 28~32.
[156]韩延明.论高校教学评估制度的构建.教育发展研究, 2006, 10A: 4~8.
[157]刘理.高校教学评估的教育价值取向分析.教育研究, 2006, 318(7): 30~34.
[158]刘智运.论中国高等教育评估制度的完善与发展——以本科教学工作水平评估为例.清华大学教育研究,2007, 28(3): 40~48.
[159]郭桂英.略论高等教育评估市场及其培育.中国高教研究, 2007, 10:16~18.
[160]刘献君.以质量为核心的教学评估体系构建——兼论我国本科教学工作水平评估.高等教育研究, 2007, 28(7): 37~43.
[161]储祖旺,戴冬秀,宋华民.高校教学评估与建立ISO9000族质量管理体系的比较研究.中国高等教育, 2006, 17: 50~51.
[162]曹勇安,高玉贵,李国文.高职院校引入ISO9000族标准的可行性辨析.教育发展研究, 2005, 11B: 95~99.
[163]孔祥沛.论我国高等教育评估面临的十大挑战.辽宁教育研究, 2007, 2: 97~99.
[164]欧阳胜,朱永东.对高校教育质量评估的反思.江西教育科研, 2007,12: 59~60.
[165]罗建国.我国高等教育政府评估的困境与对策分析.高等教育研究, 2007, 28(3): 28~34.
[166]高耀明,张萍,陈慧,等.本科教学工作水平评估对高校教学工作影响的调查研究.高等教育研究, 2006, 27(11): 85~90.
[167]袁银南,王贵成,韩广才,等.试论本科教学工作水平评估的复杂性和有效性.中国高教研究, 2006, 10: 50~51.
[168]欧阳康.对本科教学工作水平评估若干关系的思考.中国高等教育, 2006, 17: 47~49.
[169]刘振天.对本科教学评估中几个深层次问题的认识.中国高等教育, 2007, 20: 31~32.
[170]邱平.关于高职教学评估的思考.高教探索, 2007, 4: 112~113.
[171]教育部.高职高专院校人才培养工作水平评估工作指南.教高司函[2004] 16号.
[172]江苏省教育评估院.江苏省高职高专评估手册, 2006.