Three-way decisions based on decision-theoretic rough sets with dual hesitant fuzzy information

Decision-theoretic rough sets (DTRSs) as a classic model of three-way decisions have been widely applied in the area of risk decision-making. When we confront the complicated and uncertain environment, one of challenges is to estimate the loss function of DTRSs. As a new generalization of fuzzy sets, dual hesitant fuzzy sets (DHFSs) can handle uncertain information more flexibly in the process of decision making and give a new measure for the determination of loss functions of DTRSs. To have more interesting results in the context of three-way decisions, we introduce the new hesitant format of DHFSs into DTRSs and explore a new three-way decision model. Firstly, we take into account the loss functions of DTRSs with dual hesitant fuzzy elements (DHFEs) and propose a dual hesitant fuzzy DTRS model. In order to satisfy the preconditions of three-way decisions, we analyze the normalized principle of loss functions under the dual hesitant fuzzy environment. Meanwhile, some properties of the expected losses are carefully investigated. Then, we further design two approaches for deriving three-way decisions with the new DTRS model, i.e., Method 1 and Method 2, which mainly relies on the comparisons among the expected losses. Method 1 is a general method based on the scores and the accuracies of DHFEs. Method 2 is a ranking method of possibility degrees with a stochastic strategy and enriches the comparisons among the expected losses. Finally, the assessment of emergency blood transshipment is used to illustrate and compare these proposed methods.

[1]  Decui Liang,et al.  Deriving three-way decisions from intuitionistic fuzzy decision-theoretic rough sets , 2015, Inf. Sci..

[2]  Yiyu Yao,et al.  Cost-sensitive three-way email spam filtering , 2013, Journal of Intelligent Information Systems.

[3]  Zeshui Xu,et al.  Dual Hesitant Fuzzy Sets , 2012, J. Appl. Math..

[4]  Yiyu Yao,et al.  Three-Way Decisions and Cognitive Computing , 2016, Cognitive Computation.

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

[6]  Madhav Erraguntla,et al.  Better management of blood supply-chain with GIS-based analytics , 2011, Ann. Oper. Res..

[7]  Zeshui Xu,et al.  Hesitant fuzzy information aggregation in decision making , 2011, Int. J. Approx. Reason..

[8]  J. Hess,et al.  Blood use in war and disaster: lessons from the past century , 2003, Transfusion.

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

[10]  Jingtao Yao,et al.  Game-Theoretic Rough Sets , 2011, Fundam. Informaticae.

[11]  Parviz Ghandforoush,et al.  A DSS to manage platelet production supply chain for regional blood centers , 2010, Decis. Support Syst..

[12]  Francisco Herrera,et al.  Hesitant Fuzzy Linguistic Term Sets for Decision Making , 2012, IEEE Transactions on Fuzzy Systems.

[13]  Jun Ye Correlation coefficient of dual hesitant fuzzy sets and its application to multiple attribute decision making , 2014 .

[14]  Zeshui Xu,et al.  Stochastic preference analysis in numerical preference relations , 2014, Eur. J. Oper. Res..

[15]  Da Ruan,et al.  Probabilistic model criteria with decision-theoretic rough sets , 2011, Inf. Sci..

[16]  Bao Qing Hu,et al.  Three-way decisions space and three-way decisions , 2014, Inf. Sci..

[17]  Zeshui Xu,et al.  Distance and similarity measures for hesitant fuzzy sets , 2011, Inf. Sci..

[18]  Zeshui Xu,et al.  Some results for dual hesitant fuzzy sets , 2014, J. Intell. Fuzzy Syst..

[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]  Yiyu Yao,et al.  Interval sets and three-way concept analysis in incomplete contexts , 2016, International Journal of Machine Learning and Cybernetics.

[21]  Krassimir T. Atanassov,et al.  Intuitionistic fuzzy sets , 1986 .

[22]  Zhiliang Ren,et al.  A multi-attribute decision-making method with prioritization relationship and dual hesitant fuzzy decision information , 2017, Int. J. Mach. Learn. Cybern..

[23]  Yiyu Yao,et al.  A Decision Theoretic Framework for Approximating Concepts , 1992, Int. J. Man Mach. Stud..

[24]  Yiyu Yao,et al.  Decision-Theoretic Rough Set Models , 2007, RSKT.

[25]  V. Torra,et al.  A framework for linguistic logic programming , 2010 .

[26]  Hai Wang,et al.  Generalized hesitant fuzzy sets and their application in decision support system , 2013, Knowl. Based Syst..

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

[28]  Bao Qing Hu,et al.  Three-way decision spaces based on partially ordered sets and three-way decisions based on hesitant fuzzy sets , 2016, Knowl. Based Syst..

[29]  Tianrui Li,et al.  THREE-WAY GOVERNMENT DECISION ANALYSIS WITH DECISION-THEORETIC ROUGH SETS , 2012 .

[30]  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.

[31]  Nouman Azam,et al.  A three-way decision making approach to malware analysis using probabilistic rough sets , 2016, Inf. Sci..

[32]  Risto Lahdelma,et al.  SMAA-2: Stochastic Multicriteria Acceptability Analysis for Group Decision Making , 2001, Oper. Res..

[33]  Fritz Gehbauer,et al.  Optimized resource allocation for emergency response after earthquake disasters , 2000 .

[34]  Huchang Liao,et al.  Extended hesitant fuzzy hybrid weighted aggregation operators and their application in decision making , 2015, Soft Comput..

[35]  Haiyan Zhao,et al.  Decision-theoretic rough fuzzy set model and application , 2014, Inf. Sci..

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

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

[38]  Vicenç Torra,et al.  On hesitant fuzzy sets and decision , 2009, 2009 IEEE International Conference on Fuzzy Systems.

[39]  Huaxiong Li,et al.  Risk Decision Making Based on Decision-theoretic Rough Set: A Three-way View Decision Model , 2011 .

[40]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[41]  Zeshui Xu,et al.  Multiplicative Consistency of hesitant fuzzy Preference Relation and its Application in Group Decision Making , 2014, Int. J. Inf. Technol. Decis. Mak..

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

[43]  Keming Wang,et al.  Age-based policy for blood transshipment during blood shortage , 2015 .

[44]  Pushpinder Singh,et al.  A new method for solving dual hesitant fuzzy assignment problems with restrictions based on similarity measure , 2014, Appl. Soft Comput..

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

[46]  Guiwu Wei,et al.  Hesitant fuzzy prioritized operators and their application to multiple attribute decision making , 2012, Knowl. Based Syst..

[47]  B. Farhadinia,et al.  Correlation for Dual Hesitant Fuzzy Sets and Dual Interval‐Valued Hesitant Fuzzy Sets , 2014, Int. J. Intell. Syst..

[48]  Yiyu Yao,et al.  Granular Computing and Sequential Three-Way Decisions , 2013, RSKT.

[49]  Witold Pedrycz,et al.  Granular Computing: Analysis and Design of Intelligent Systems , 2013 .