14 THE USE OF ROUGH SETS

[1]  R. Luce.  Semiorders and a Theory of Utility Discrimination , 1956 .

[2]  R. Słowiński.  Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory , 1992 .

[3]  Jerzy W. Grzymala-Busse,et al.  LERS-A System for Learning from Examples Based on Rough Sets , 1992, Intelligent Decision Support.

[4]  R. Słowiński,et al.  Rough sets approach to analysis of data from peritoneal lavage in acute pancreatitis. , 1988, Medical informatics = Medecine et informatique.

[5]  Salvatore Greco,et al.  Rough Set Approach to Multi-Attribute Choice and Ranking Problems , 1997 .

[6]  D. Bouyssou.  Outranking Relations: Do They Have Special Properties? , 1996 .

[7]  Andrzej Skowron,et al.  Rough Mereology , 1994, ISMIS.

[8]  P. Fishburn.  Methods of Estimating Additive Utilities , 1967 .

[9]  Alexis Tsoukiàs,et al.  Extended preference structures in MCDA , 1997 .

[10]  B. Roy.  THE OUTRANKING APPROACH AND THE FOUNDATIONS OF ELECTRE METHODS , 1991 .

[11]  Jerzy W. Grzymala-Busse,et al.  A New Version of the Rule Induction System LERS , 1997, Fundam. Informaticae.

[12]  Roman Slowinski,et al.  Rough-Set Reasoning about Uncertain Data , 1996, Fundam. Informaticae.

[13]  A. Tversky.  Features of Similarity , 1977 .

[14]  Jaap Van Brakel,et al.  Foundations of measurement , 1983 .

[15]  L. Shapley.  A Value for n-person Games , 1988 .

[16]  Z. Pawlak,et al.  Rough set approach to multi-attribute decision analysis , 1994 .

[17]  R. L. Keeney,et al.  Decisions with Multiple Objectives: Preferences and Value Trade-Offs , 1977, IEEE Transactions on Systems, Man, and Cybernetics.

[18]  Salvatore Greco,et al.  Fuzzy Similarity Relation as a Basis for Rough Approximations , 1998, Rough Sets and Current Trends in Computing.

[19]  Salvatore Greco,et al.  A New Rough Set Approach to Multicriteria and Multiattribute Classification , 1998, Rough Sets and Current Trends in Computing.

[20]  Andrzej Skowron,et al.  Decision Algorithms: A Survey of Rough Set - Theoretic Methods , 1997, Fundam. Informaticae.

[21]  Z. Pawlak.  Rough set approach to knowledge-based decision support , 1997 .

[22]  P. Slovic.  Choice Between Equally Valued Alternatives. , 1975 .

[23]  S. Greco,et al.  Exploitation of A rough approximation of the outranking relation in multicriteria choice and ranking , 1998 .

[24]  Jerzy W. Grzymala-Busse,et al.  Rough Sets , 1995, Commun. ACM.

[25]  Marc Roubens,et al.  Fuzzy Preference Modelling and Multicriteria Decision Support , 1994, Theory and Decision Library.

[26]  Marzena Kryszkiewicz,et al.  Computation of Reducts of Composed Information Systems , 1996, Fundam. Informaticae.

[27]  A. Tversky.  Intransitivity of preferences. , 1969 .

[28]  Wojciech Ziarko,et al.  An Incremental Learning Algorithm for Constructing Decision Rules , 1993, RSKD.

[29]  A. Tsoukiàs,et al.  A new axiomatic foundation of partial comparability , 1995 .

[30]  D. Dubois,et al.  ROUGH FUZZY SETS AND FUZZY ROUGH SETS , 1990 .

[31]  Roman Slowinski,et al.  'Roughdas' and 'Roughclass' Software Implementations of the Rough Sets Approach , 1992, Intelligent Decision Support.

[32]  Daniel Vanderpooten,et al.  A Generalized Definition of Rough Approximations Based on Similarity , 2000, IEEE Trans. Knowl. Data Eng..

[33]  S. Greco,et al.  Rough Approximation of a Preference Relation in a Pairwise Comparison Table , 1998 .

[34]  S. Marcus.  Tolerance rough sets, Čech topologies, learning processes , 1994 .

[35]  Andrzej Skowron,et al.  Dynamic Reducts as a Tool for Extracting Laws from Decisions Tables , 1994, ISMIS.

[36]  Jerzy W. Grzymala-Busse,et al.  Global discretization of continuous attributes as preprocessing for machine learning , 1996, Int. J. Approx. Reason..

[37]  P. Fishburn.  Nontransitive additive conjoint measurement , 1991 .

[38]  Z. Pawlak.  Rough sets and fuzzy sets , 1985 .

[39]  Roman Slowinski,et al.  Handling Various Types of Uncertainty in the Rough Set Approach , 1993, RSKD.

[40]  Roman Słowiński,et al.  A New Rough Set Approach to Evaluation of Bankruptcy Risk , 1998 .

[41]  J. Siskos.  Assessing a set of additive utility functions for multicriteria decision-making , 1982 .

[42]  Michel Grabisch,et al.  K-order Additive Discrete Fuzzy Measures and Their Representation , 1997, Fuzzy Sets Syst..

[43]  M. Grabisch.  The application of fuzzy integrals in multicriteria decision making , 1996 .

[44]  Z. Pawlak.  Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .

[45]  Ivan Bratko,et al.  Machine Learning and Data Mining; Methods and Applications , 1998 .

[46]  Bernard Roy,et al.  Decision science or decision-aid science? , 1993 .

[47]  R. Słowiński,et al.  Learning Decision Rules from Similarity Based Rough Approximations , 1998 .

[48]  Philippe Vincke,et al.  Multicriteria Decision-Aid , 1992 .

[49]  Jerzy Stefanowski,et al.  Rough classification in incomplete information systems , 1989 .

[50]  Didier Dubois,et al.  Putting Rough Sets and Fuzzy Sets Together , 1992, Intelligent Decision Support.

[51]  Roman Slowinski,et al.  Sensitivity Analysis of Rough Classification , 1990, Int. J. Man Mach. Stud..

[52]  R. Słowiński,et al.  Discriminant versus rough sets approach to vague data analysis , 1992 .

[53]  M. Roubens,et al.  Equivalent representations of a set function with application to decision making , 1997, Proceedings of 6th International Fuzzy Systems Conference.

[54]  Salvatore Greco,et al.  Rough approximation of a preference relation by dominance relations , 1999, Eur. J. Oper. Res..

[55]  Andrzej Skowron,et al.  Boolean Reasoning for Decision Rules Generation , 1993, ISMIS.

[56]  P. Schoemaker.  The Expected Utility Model: Its Variants, Purposes, Evidence and Limitations , 1982 .

[57]  Jean-Luc Marichal,et al.  Interaction between criteria through the use of fuzzy measures , 1996 .