Connections between rough set theory and Dempster-Shafer theory of evidence

In rough set theory there exists a pair of approximation operators, the upper and lower approximations, whereas in Dempster-Shafer theory of evidence there exists a dual pair of uncertainty measures, the plausibility and belief functions. It seems that there is some kind of natural connection between the two theories. The purpose of this paper is to establish the relationship between rough set theory and Dempster-Shafer theory of evidence. Various generalizations of the Dempster-Shafer belief structure and their induced uncertainty measures, the plausibility and belief functions, are first reviewed and examined. Generalizations of Pawlak approximation space and their induced approximation operators, the upper and lower approximations, are then summarized. Concepts of random rough sets, which include the mechanisms of numeric and non-numeric aspects of uncertain knowledge, are then proposed. Notions of the Dempster-Shafer theory of evidence within the framework of rough set theory are subsequently formed and interpreted. It is demonstrated that various belief structures are associated with various rough approximation spaces such that different dual pairs of upper and lower approximation operators induced by the rough approximation spaces may be used to interpret the corresponding dual pairs of plausibility and belief functions induced by the belief structures.

[1]  Slawomir T. Wierzchon,et al.  A New Qualitative Rough-Set Approach to Modeling Belief Functions , 1998, Rough Sets and Current Trends in Computing.

[2]  Andrzej Skowron,et al.  The rough sets theory and evidence theory , 1990 .

[3]  George J. Klir,et al.  A principle of uncertainty and information invariance , 1990 .

[4]  T. Y. Lin,et al.  Granular Computing on Binary Relations II Rough Set Representations and Belief Functions , 1998 .

[5]  Abraham Kandel,et al.  Constraints on belief functions imposed by fuzzy random variables , 1995, IEEE Trans. Syst. Man Cybern..

[6]  Philippe Smets,et al.  The Combination of Evidence in the Transferable Belief Model , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Yiyu Yao,et al.  Data Mining Using Extensions of the Rough Set Model , 1998, J. Am. Soc. Inf. Sci..

[8]  Nehad N. Morsi,et al.  Axiomatics for fuzzy rough sets , 1998, Fuzzy Sets Syst..

[9]  Ronald R. Yager,et al.  Generalized probabilities of fuzzy events from fuzzy belief structures , 1982, Inf. Sci..

[10]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[11]  D. Dubois,et al.  Properties of measures of information in evidence and possibility theories , 1987 .

[12]  J. Jaffray On the Maximum of Conditional Entropy for Upper/Lower Probabilities Generated by Random Sets , 1997 .

[13]  María Angeles Gil,et al.  Fuzzy random variables , 2001, Inf. Sci..

[14]  Marzena Kryszkiewicz,et al.  Rough Set Approach to Incomplete Information Systems , 1998, Inf. Sci..

[15]  Lotfi A. Zadeh,et al.  Fuzzy sets and information granularity , 1996 .

[16]  Philippe Smets,et al.  The degree of belief in a fuzzy event , 1981, Inf. Sci..

[17]  Yiyu Yao,et al.  Interpretation of Belief Functions in The Theory of Rough Sets , 1998, Inf. Sci..

[18]  Thierry Denoeux,et al.  Modeling vague beliefs using fuzzy-valued belief structures , 2000, Fuzzy Sets Syst..

[19]  Mitsuru Ishizuka,et al.  Inference procedures under uncertainty for the problem-reduction method , 1982, Inf. Sci..

[20]  L. Zadeh Fuzzy sets as a basis for a theory of possibility , 1999 .

[21]  Huibert Kwakernaak,et al.  Fuzzy random variables - I. definitions and theorems , 1978, Inf. Sci..

[22]  T. Denœux Modeling vague beliefs using fuzzy-valued belief structures , 2000 .

[23]  Y. Yao,et al.  Generalized Rough Set Models , 1998 .

[24]  Marzena Kryszkiewicz,et al.  Rules in Incomplete Information Systems , 1999, Inf. Sci..

[25]  Yiyu Yao,et al.  Relational Interpretations of Neigborhood Operators and Rough Set Approximation Operators , 1998, Inf. Sci..

[26]  Huibert Kwakernaak,et al.  Fuzzy random variables--II. Algorithms and examples for the discrete case , 1979, Inf. Sci..

[27]  King-Sun Fu,et al.  An Inexact Inference for Damage Assessment of Existing Structures , 1985, Int. J. Man Mach. Stud..

[28]  Hung T. Nguyen,et al.  Some mathematical structures for computational information , 2000, Inf. Sci..

[29]  L. Zadeh Probability measures of Fuzzy events , 1968 .

[30]  J. Recasens,et al.  UPPER AND LOWER APPROXIMATIONS OF FUZZY SETS , 2000 .

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

[32]  Tsau Young Lin,et al.  Fuzzy Partitions II: Belief Functions. A Probalistic View , 1998, Rough Sets and Current Trends in Computing.

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

[34]  Yiyu Yao,et al.  Two views of the theory of rough sets in finite universes , 1996, Int. J. Approx. Reason..

[35]  Andrzej Skowron,et al.  From the Rough Set Theory to the Evidence Theory , 1991 .

[36]  John Yen,et al.  Computing generalized belief functions for continuous fuzzy sets , 1992, Int. J. Approx. Reason..

[37]  G. Matheron Random Sets and Integral Geometry , 1976 .

[38]  Janusz Zalewski,et al.  Rough sets: Theoretical aspects of reasoning about data , 1996 .

[39]  D. Dubois,et al.  Fundamentals of fuzzy sets , 2000 .