Conflict Analysis for Pythagorean Fuzzy Information Systems

Pythagorean fuzzy sets as generalizations of intuitionistic fuzzy sets are effective for dealing with uncertainty information, but little effort has been paid to conflict analysis of Pythagorean fuzzy information systems. In this paper, we present the concepts of the maximum positive alliance, central alliance, and negative alliance with the two thresholds \(\alpha \) and \(\beta \). Then we show how to compute the thresholds \(\alpha \) and \(\beta \) for conflict analysis based on decision-theoretic rough set theory. Finally, we employ several examples to illustrate how to compute the maximum positive alliance, central alliance, and negative alliance from the view of matrix.

[1]  Ronald R. Yager,et al.  Pythagorean Membership Grades in Multicriteria Decision Making , 2014, IEEE Transactions on Fuzzy Systems.

[2]  Rafal Deja.  Conflict analysis , 2002, Int. J. Intell. Syst..

[3]  Andrzej Skowron,et al.  Requiremnents Interaction an Conflicts A Rough Set Approach , 2007, 2007 IEEE Symposium on Foundations of Computational Intelligence.

[4]  Mingjie Cai,et al.  Knowledge reduction of dynamic covering decision information systems when varying covering cardinalities , 2016, Inf. Sci..

[5]  Jing-Yu Yang,et al.  Cost-sensitive rough set approach , 2016, Inf. Sci..

[6]  Degang Chen,et al.  Minimal decision cost reduct in fuzzy decision-theoretic rough set model , 2017, Knowl. Based Syst..

[7]  Nouman Azam,et al.  A three-way approach for learning rules in automatic knowledge-based topic models , 2017, Int. J. Approx. Reason..

[8]  Andrzej Skowron,et al.  On some conflict models and conflict resolution , 2002 .

[9]  Zeshui Xu,et al.  Extension of TOPSIS to Multiple Criteria Decision Making with Pythagorean Fuzzy Sets , 2014, Int. J. Intell. Syst..

[10]  Ronald R. Yager,et al.  Suggesting Recommendations Using Pythagorean Fuzzy Sets illustrated Using Netflix Movie Data , 2014, IPMU.

[11]  Francisco Herrera,et al.  A Historical Account of Types of Fuzzy Sets and Their Relationships , 2016, IEEE Transactions on Fuzzy Systems.

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

[13]  Tsau Young Lin.  Granular computing on binary relations: Analysis of conflict and Chinese wall security policy , 2002 .

[14]  Lucimário Gois de Oliveira Silva,et al.  A multicriteria approach for analysis of conflicts in evidence theory , 2016, Inf. Sci..

[15]  Haiyan Zhao,et al.  Rough set-based conflict analysis model and method over two universes , 2016, Inf. Sci..

[16]  Andrzej Skowron,et al.  Requirements Interaction and Conflicts : A Rough Set Approach , 2007 .

[17]  Bing Huang,et al.  Cost-sensitive sequential three-way decision modeling using a deep neural network , 2017, Int. J. Approx. Reason..

[18]  Gui-Long Liu,et al.  The Axiomatization of the Rough Set Upper Approximation Operations , 2006, Fundam. Informaticae.

[19]  Zdzislaw Pawlak,et al.  Some remarks on conflict analysis , 2005, Eur. J. Oper. Res..

[20]  Gleb Beliakov,et al.  Averaging aggregation functions for preferences expressed as Pythagorean membership grades and fuzzy orthopairs , 2014, 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[21]  Yutaka Maeda,et al.  Interval Density Functions in Conflict Analysis , 1999, RSFDGrC.

[22]  Duoqian Miao,et al.  A variable precision rough set model based on the granularity of tolerance relation , 2016, Knowl. Based Syst..