Three-way group decision making based on multigranulation fuzzy decision-theoretic rough set over two universes

Decision-theoretic rough set provides a new perspective to handle decision-making problems under uncertainty and risk. The three-way decision theory proposed by Yao is based on rough set theory and is a natural extension of the classical two-way decision approach. In this paper, we introduce the idea of decision-theoretic rough set into multigranulation approximation space and explore the rough approximation of a fuzzy decision object under the framework of two universes. We construct a variable precision multigranulation fuzzy decision-theoretic rough set over two universes by using the concept of an arbitrary binary fuzzy relation class between two different universes and the probability measurement of a fuzzy event. Several interesting properties of the proposed model are addressed and the decision rules are also deduced using the concept of three-way decision-making over two universes. Moreover, two special types of optimistic and pessimistic models are given by using different precision parameters. We then present a new approach to multiple criteria group decision making problems, based on variable precision multigranulation fuzzy decision-theoretic rough set over two universes. Meanwhile, we establish a cost-based method for sorting among all alternatives of group decision-making problems. Finally, an example of handling a medical diagnosis problem illustrates this approach. A new multigranulation fuzzy decision-theoretic rough set over two universes was defined.The relationship between the proposed model with the existing decision-theoretic rough set models was established.The three-way decision was deduced based on the multigranulation fuzzy decision-theoretic rough set over two universes.Multigranulation fuzzy decision-theoretic rough set-based three-way group decision making method was established over two universes.

[1]  Yiyu Yao,et al.  The superiority of three-way decisions in probabilistic rough set models , 2011, Inf. Sci..

[2]  Witold Pedrycz,et al.  International Journal of Approximate Reasoning Triangular Fuzzy Decision-theoretic Rough Sets , 2022 .

[3]  Jingtao Yao,et al.  Modelling Multi-agent Three-way Decisions with Decision-theoretic Rough Sets , 2012, Fundam. Informaticae.

[4]  Bingzhen Sun,et al.  An approach to consensus measurement of linguistic preference relations in multi-attribute group decision making and application , 2015 .

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

[6]  Yiyu Yao,et al.  Three-way Investment Decisions with Decision-theoretic Rough Sets , 2011, Int. J. Comput. Intell. Syst..

[7]  Yin-Feng Xu,et al.  Maximum expert consensus models with linear cost function and aggregation operators , 2013, Comput. Ind. Eng..

[8]  Yiyu Yao,et al.  An Outline of a Theory of Three-Way Decisions , 2012, RSCTC.

[9]  Rob Goudey,et al.  Do statistical inferences allowing three alternative decisions give better feedback for environmentally precautionary decision-making? , 2007, Journal of environmental management.

[10]  Guoyin Wang,et al.  An automatic method to determine the number of clusters using decision-theoretic rough set , 2014, Int. J. Approx. Reason..

[11]  Shusaku Tsumoto,et al.  Automated Extraction of Medical Expert System Rules from Clinical Databases on Rough Set Theory , 1998, Inf. Sci..

[12]  Weihua Xu,et al.  Double-quantitative decision-theoretic rough set , 2015, Inf. Sci..

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

[14]  Qinghua Hu,et al.  Neighborhood rough set based heterogeneous feature subset selection , 2008, Inf. Sci..

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

[16]  Yiyu Yao,et al.  Probabilistic rough set approximations , 2008, Int. J. Approx. Reason..

[17]  Yin-Feng Xu,et al.  Multiple attribute consensus rules with minimum adjustments to support consensus reaching , 2014, Knowl. Based Syst..

[18]  Decui Liang,et al.  Three-way group decisions with decision-theoretic rough sets , 2016, Inf. Sci..

[19]  Decui Liang,et al.  A Novel Risk Decision Making Based on Decision-Theoretic Rough Sets Under Hesitant Fuzzy Information , 2015, IEEE Transactions on Fuzzy Systems.

[20]  Decui Liang,et al.  A novel three-way decision model based on incomplete information system , 2016, Knowl. Based Syst..

[21]  Weihua Xu,et al.  Multi-granulation fuzzy rough sets , 2014, J. Intell. Fuzzy Syst..

[22]  Yiyu Yao,et al.  MGRS: A multi-granulation rough set , 2010, Inf. Sci..

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

[24]  Qin Liu,et al.  An approach to decision making based on intuitionistic fuzzy rough sets over two universes , 2013, J. Oper. Res. Soc..

[25]  Yiyu Yao,et al.  Data mining using extensions of the rough set model , 1998, KDD 1998.

[26]  Bingzhen Sun,et al.  On relationship between probabilistic rough set and Bayesian risk decision over two universes , 2012, Int. J. Gen. Syst..

[27]  Caihui Liu,et al.  On multi-granulation covering rough sets , 2014, Int. J. Approx. Reason..

[28]  S. K. Wong,et al.  A NON-NUMERIC APPROACH TO UNCERTAIN REASONING , 1995 .

[29]  J. Kassirer,et al.  The threshold approach to clinical decision making. , 1980, The New England journal of medicine.

[30]  Tianrui Li,et al.  Composite rough sets for dynamic data mining , 2014, Inf. Sci..

[31]  Ingoo Han,et al.  The Extraction of Trading Rules From Stock Market Data Using Rough Sets , 2001, Expert Syst. J. Knowl. Eng..

[32]  Jiye Liang,et al.  A fuzzy multigranulation decision-theoretic approach to multi-source fuzzy information systems , 2016, Knowl. Based Syst..

[33]  Decui Liang,et al.  Incorporating logistic regression to decision-theoretic rough sets for classifications , 2014, Int. J. Approx. Reason..

[34]  Witold Pedrycz,et al.  A method based on PSO and granular computing of linguistic information to solve group decision making problems defined in heterogeneous contexts , 2013, Eur. J. Oper. Res..

[35]  Nouman Azam,et al.  Web-Based Medical Decision Support Systems for Three-Way Medical Decision Making With Game-Theoretic Rough Sets , 2015, IEEE Transactions on Fuzzy Systems.

[36]  Weihua Xu,et al.  Multiple granulation rough set approach to ordered information systems , 2012, Int. J. Gen. Syst..

[37]  Bingzhen Sun,et al.  Fuzzy rough set on probabilistic approximation space over two universes and its application to emergency decision‐making , 2015, Expert Syst. J. Knowl. Eng..

[38]  Jianming Zhan,et al.  A new rough set theory: rough soft hemirings , 2015, J. Intell. Fuzzy Syst..

[39]  Min Chen,et al.  Rough Cluster Quality Index Based on Decision Theory , 2009, IEEE Transactions on Knowledge and Data Engineering.

[40]  Wei-Zhi Wu,et al.  Constructive and axiomatic approaches of fuzzy approximation operators , 2004, Inf. Sci..

[41]  Yiyu Yao,et al.  Three-way decisions with probabilistic rough sets , 2010, Inf. Sci..

[42]  Yee Leung,et al.  Theory and applications of granular labelled partitions in multi-scale decision tables , 2011, Inf. Sci..

[43]  Jiye Liang,et al.  Pessimistic rough set based decisions: A multigranulation fusion strategy , 2014, Inf. Sci..

[44]  Yiyu Yao,et al.  Bayesian Decision Theory for Dominance-Based Rough Set Approach , 2007, RSKT.

[45]  Jiye Liang,et al.  Incomplete Multigranulation Rough Set , 2010, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[46]  H. M. Abu-Donia,et al.  Multi knowledge based rough approximations and applications , 2012, Knowl. Based Syst..

[47]  Guoyin Wang,et al.  A tree-based incremental overlapping clustering method using the three-way decision theory , 2016, Knowl. Based Syst..

[48]  Yiyu Yao,et al.  Decision-theoretic three-way approximations of fuzzy sets , 2014, Inf. Sci..

[49]  Yin-Feng Xu,et al.  Minimum-Cost Consensus Models Under Aggregation Operators , 2011, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[50]  Bingzhen Sun,et al.  Multigranulation rough set theory over two universes , 2015, J. Intell. Fuzzy Syst..

[51]  Weihua Xu,et al.  Multi-granulation Fuzzy Rough Sets in a Fuzzy Tolerance Approximation Space , 2011 .

[52]  Rossella Agliardi,et al.  Theory of measure , 2014 .

[53]  Yiyu Yao,et al.  ON MODELING UNCERTAINTY WITH INTERVAL STRUCTURES , 1995, Comput. Intell..

[54]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[55]  Jiye Liang,et al.  International Journal of Approximate Reasoning Multigranulation Decision-theoretic Rough Sets , 2022 .

[56]  Haiyan Zhao,et al.  An approach to emergency decision making based on decision-theoretic rough set over two universes , 2016, Soft Comput..

[57]  Stein W. Wallace,et al.  Decision Making Under Uncertainty: Is Sensitivity Analysis of Any Use? , 2000, Oper. Res..

[58]  Yuhua Qian,et al.  NMGRS: Neighborhood-based multigranulation rough sets , 2012, Int. J. Approx. Reason..

[59]  Bing Huang,et al.  Intuitionistic fuzzy multigranulation rough sets , 2014, Inf. Sci..

[60]  Weihua Xu,et al.  Multigranulation Decision-theoretic Rough Set in Ordered Information System , 2015 .

[61]  Keith W. Hipel,et al.  A Decision Rule Aggregation Approach to Multiple Criteria-Multiple Participant Sorting , 2012 .

[62]  Y. Yao Information granulation and rough set approximation , 2001 .

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

[64]  Weihua Xu,et al.  Multi-granulation rough sets based on tolerance relations , 2013, Soft Computing.

[65]  Fan Min,et al.  Three-way recommender systems based on random forests , 2016, Knowl. Based Syst..

[66]  K. Arrow Social Choice and Individual Values , 1951 .

[67]  Nouman Azam,et al.  Analyzing uncertainties of probabilistic rough set regions with game-theoretic rough sets , 2014, Int. J. Approx. Reason..

[68]  Qiang Shen,et al.  Fuzzy-Rough Sets Assisted Attribute Selection , 2007, IEEE Transactions on Fuzzy Systems.

[69]  Weihua Xu,et al.  A Generalized Multi-granulation Rough Set Approach , 2011, ICIC.