Deriving three-way decisions from intuitionistic fuzzy decision-theoretic rough sets

Three-way decisions with decision-theoretic rough sets (DTRSs) provide a new methodology to confront risk decision problems. The risk is associated with the loss function of DTRSs. Under the intuitionistic fuzzy environment, we combine the loss functions of DTRSs with intuitionistic fuzzy sets (IFSs). Considering the new evaluation format of loss function with intuitionistic fuzzy numbers (IFNs), we propose a naive model of intuitionistic fuzzy decision-theoretic rough sets (IFDTRSs) and elaborate its relevant properties in advance. At this point, a critical issue is the determination of three-way decisions. In the frame of IFDTRSs, we then explore deriving three-way decisions for single-period decision making. Based on the positive and negative characteristics of IFNs, we design three strategies to address IFNs and derive corresponding three-way decisions. Meanwhile, we compare the three strategies and summarize their own applicabilities. In order to accommodate multi-period scenarios, we further extend IFDTRSs to the multi-period situation. With the aid of the results of the single period decision making, we analyze three aggregation operations of IFDTRSs for multi-period information, which are DIFWA, DIFPA and DIFOA, respectively. By comparing these operations, an algorithm for deriving three-way decisions in multi-period decision making is designed. These results help us to make a reasonable decision in the intuitionistic fuzzy environment. Finally, an example is presented to elaborate on three-way decisions with IFDTRSs.

[1]  Decui Liang,et al.  Systematic studies on three-way decisions with interval-valued decision-theoretic rough sets , 2014, Inf. Sci..

[2]  Zeshui Xu,et al.  Clustering algorithm for intuitionistic fuzzy sets , 2008, Inf. Sci..

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

[4]  Ronald R. Yager,et al.  Some aspects of intuitionistic fuzzy sets , 2009, Fuzzy Optim. Decis. Mak..

[5]  Zeshui Xu,et al.  Intuitionistic preference relations and their application in group decision making , 2007, Inf. Sci..

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

[7]  Bing Huang,et al.  Cost-Sensitive Classification Based on Decision-Theoretic Rough Set Model , 2012, RSKT.

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

[9]  B. C. Brookes,et al.  Information Sciences , 2020, Cognitive Skills You Need for the 21st Century.

[10]  XuZeshui On multi-period multi-attribute decision making , 2008 .

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

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

[13]  Zeshui Xu,et al.  Dynamic intuitionistic fuzzy multi-attribute decision making , 2008, Int. J. Approx. Reason..

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

[15]  William Zhu,et al.  Attribute reduction of data with error ranges and test costs , 2012, Inf. Sci..

[16]  Ioannis K. Vlachos,et al.  Intuitionistic fuzzy information - Applications to pattern recognition , 2007, Pattern Recognit. Lett..

[17]  Deng-Feng Li,et al.  Multiattribute decision making models and methods using intuitionistic fuzzy sets , 2005, J. Comput. Syst. Sci..

[18]  Zeshui Xu,et al.  Some geometric aggregation operators based on intuitionistic fuzzy sets , 2006, Int. J. Gen. Syst..

[19]  Alexander Okhotin,et al.  On Language Equations with One-sided Concatenation , 2013, Fundam. Informaticae.

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

[21]  Jingtao Yao,et al.  Web-Based Support Systems with Rough Set Analysis , 2007, RSEISP.

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

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

[24]  Chengqi Zhang,et al.  An information filtering model on the Web and its application in JobAgent , 2000, Knowl. Based Syst..

[25]  Zeshui Xu,et al.  Intuitionistic Fuzzy Aggregation Operators , 2007, IEEE Transactions on Fuzzy Systems.

[26]  Yiyu Yao Three-Way Decisions Using Rough Sets , 2012 .

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

[28]  Ting-Yu Chen,et al.  Bivariate models of optimism and pessimism in multi-criteria decision-making based on intuitionistic fuzzy sets , 2011, Inf. Sci..

[29]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[30]  Huawen Liu,et al.  Multi-criteria decision-making methods based on intuitionistic fuzzy sets , 2007, Eur. J. Oper. Res..

[31]  Dun Liu,et al.  Non-Monotonic Attribute Reduction in Decision-Theoretic Rough Sets , 2013, Fundam. Informaticae.

[32]  Decui Liang,et al.  Three-Way Decisions in Dynamic Decision-Theoretic Rough Sets , 2013, RSKT.

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

[34]  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.

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

[36]  Zhenmin Tang,et al.  On an optimization representation of decision-theoretic rough set model , 2014, Int. J. Approx. Reason..

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

[38]  Bing Zhou,et al.  Multi-class decision-theoretic rough sets , 2014, Int. J. Approx. Reason..

[39]  Nan Zhang,et al.  Hierarchical rough decision theoretic framework for text classification , 2010, 9th IEEE International Conference on Cognitive Informatics (ICCI'10).

[40]  Zeshui Xu,et al.  Recent advances in intuitionistic fuzzy information aggregation , 2010, Fuzzy Optim. Decis. Mak..

[41]  Diyar Akay,et al.  A biparametric similarity measure on intuitionistic fuzzy sets with applications to pattern recognition , 2014, Inf. Sci..

[42]  Ronald R. Yager,et al.  OWA aggregation of intuitionistic fuzzy sets , 2009, Int. J. Gen. Syst..

[43]  Zeshui Xu,et al.  On multi-period multi-attribute decision making , 2008, Knowl. Based Syst..

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

[45]  Duoqian Miao,et al.  Reduction target structure-based hierarchical attribute reduction for two-category decision-theoretic rough sets , 2014, Inf. Sci..

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

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

[48]  Zeshui Xu,et al.  Choquet integrals of weighted intuitionistic fuzzy information , 2010, Inf. Sci..

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

[50]  Decui Liang,et al.  Decision-Theoretic Rough Sets with Probabilistic Distribution , 2012, RSKT.

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

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

[53]  Yiyu Yao,et al.  Sequential three-way decisions with probabilistic rough sets , 2011, IEEE 10th International Conference on Cognitive Informatics and Cognitive Computing (ICCI-CC'11).

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

[55]  Guoyin Wang,et al.  Decision region distribution preservation reduction in decision-theoretic rough set model , 2014, Inf. Sci..

[56]  A. K. Ray,et al.  A new measure using intuitionistic fuzzy set theory and its application to edge detection , 2008, Appl. Soft Comput..

[57]  Huaxiong Li,et al.  Risk Decision Making Based on Decision-theoretic Rough Set: A Three-way View Decision Model , 2011, Int. J. Comput. Intell. Syst..

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

[59]  Yiyu Yao,et al.  An Information-Theoretic Interpretation of Thresholds in Probabilistic Rough Sets , 2012, RSKT.

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

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

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

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

[64]  Zhenmin Tang,et al.  Minimum cost attribute reduction in decision-theoretic rough set models , 2013, Inf. Sci..