The long payback period for medical care products prevents investors from immediately recognizing risks arising throughout the entire period. To avoid risk loss, delay decision should be introduced to investment decision. In this study, we illustrate investment decision from the view of three-way group decisions. Linguistic scale is widely used during assessment, but the randomness and fuzziness of linguistic information are ignored. To cover these defects, this study introduces cloud to three-way group decisions and further extends cloud to medical care product investment decision in which the weights of experts are unknown. In our proposed model, the loss functions and conditional probability described by linguistic values in decision theoretic rough sets are converted to clouds, which can handle the fuzziness and randomness of linguistic information. The corresponding three-way decision rules are also derived from a cloud perspective. In addition, we define a new derivation degree based on the score function of cloud to determine the weights of experts in three-way group decisions. To validate the feasibility of our model, comparisons with the existing model are presented.
LiuD., YaoY.Y. and LiT.R., Three-way investment decisions with decision-theoretic rough sets, International Journal of Computational Intelligence Systems4 (2011), 66–74.
2.
XieG., YueW.Y., WangS.Y. and LaiK.K., Dynamic risk management in petroleum project investment based on a variable precision rough set model, Technological Forecasting and Social Change77 (2010), 891–901.
3.
ZhangH.R. and MinF., Three-way recommender systems based on random forests, Knowledge-Based Systems91 (2016), 275–286.
4.
ZhangH.R., MinF. and ShiB., Regression-based three-way recommendation, Information Sciences378 (2017), 444–461.
5.
YaoY.Y., Three-way decision: An interpretation of rules in rough set theory, International Conference on Rough Sets and Knowledge Technology, 2009, pp. 642–649.
6.
YaoY.Y., An outline of a theory of three-way decisions, Rough Sets and Current Trends in Computing (2012), 1–17.
7.
YaoY.Y., The superiority of three-way decisions in probabilistic rough set models, Information Sciences181 (2011), 1080–1096.
8.
YaoY.Y., Three-way decisions with probabilistic rough sets, Information Sciences180 (2010), 341–353.
9.
LiuD., LiT.R. and LiH.X., A multiple-category classification approach with decision-theoretic rough sets, Fundamenta Informaticae115 (2012), 173–188.
10.
ZhouB., Multi-class decision-theoretic rough sets, International Journal of Approximate Reasoning55 (2014), 211–224.
11.
LiangD.C. and LiuD., Deriving three-way decisions from intuitionistic fuzzy decision-theoretic rough sets, Information Sciences300 (2015), 28–48.
12.
ZhouB., YaoY.Y. and LuoJ.G., Cost-sensitive three-way email spam filtering, Journal of Intelligent Information Systems42 (2014), 19–45.
13.
HuJ.H., YangY. and ChenX.H., A novel TODIM method-based three-way decision model for medical treatment selection, International Journal of Fuzzy Systems (2017). DOI: 10.1007/s40815-017-0320-3.
14.
LiuD., LiangD.C. and WangC.C., A novel three-way decision model based on incomplete information system, Knowledge-Based Systems91 (2016), 32–45.
15.
JiaX.Y., TangZ.M., LiaoW.H. and ShangL., On an optimization representation of decision-theoretic rough set model, International Journal of Approximate Reasoning55 (2014), 156–166.
16.
LiangD.C., LiuD., PedryczW. and HuP., Triangular fuzzy decision-theoretic rough sets, International Journal of Approximate Reasoning54 (2013), 1087–1106.
17.
DengX.F. and YaoY.Y., Decision-theoretic three-way approximations of fuzzy sets, Information Sciences279 (2014), 702–715.
18.
LiangD.C., PedryczW., LiuD. and HuP., Three-way decisions based on decision-theoretic rough sets under linguistic assessment with the aid of group decision making, Applied Soft Computing29 (2015), 256–269.
19.
ZhangH.Y., PengH.G., WangJ. and WangJ.Q., An extended outranking approach for multi-criteria decision-making problems with linguistic intuitionistic fuzzy numbers, Applied Soft Computing56 (2017), 462–474.
20.
NieR.X., WangJ.Q. and LiL., A shareholder voting method for proxy advisory firm selection based on 2-tuple linguistic picture preference relation, Applied Soft Computing60 (2017), 520–539.
21.
WangJ., WangJ.Q., ZhangH.Y. and ChenX.H., Distance-based multi-criteria group decision-making approachs with multi-hestant fuzzy linguistic information, International Journal of Information Technology & Decision Making16 (2017), 1069–1099.
22.
YuD.J., LiD.F., MerigóJ.M. and FangL.C., Mapping development of linguistic decision making studies, Journal of Intelligent & Fuzzy Systems30 (2016), 2727–2736.
23.
ZhouL.G., WuJ.X. and ChenH.Y., Linguistic continuous ordered weighted distance measure and its application to multiple attributes group decision making, Applied Soft Computing25 (2014), 266–276.
24.
ZhouL.G. and YouC.H., The induced linguistic continuous ordered weighted geometric operator and its application to group decision making, Computers & Industrial Engineering66 (2013), 222–232.
25.
MerigóJ.M., Gil LafuenteA.M., ZhouL.G. and ChenH.Y., Induced and linguistic generalized aggregation operators and their application in linguistic group decision making, Group Decision and Negotiation21 (2012), 531–549.
26.
ZhouL.G. and ChenH.Y., A generalization of the power aggregation operators for linguistic environment and its application in group decision making, Knowledge-Based Systems26 (2012), 216–224.
27.
ZhouL.G. and ChenH.Y., On compatibility of uncertain additive linguistic preference relations based on the linguistic COWA operator, Applied Soft Computing13 (2013), 3668–3682.
28.
MassanetS., RieraJ.V., TorrensJ. and Herrera ViedmaE., A new linguistic computational model based on discrete fuzzy numbers for computing with words, Information Sciences258 (2014), 277–290.
29.
RieraJ.V., MassanetS., Herrera ViedmaE. and TorrensJ., Some interesting properties of the fuzzy linguistic model based on discrete fuzzy numbers to manage hesitant fuzzy linguistic information, Applied Soft Computing36 (2015), 383–391.
30.
MerigoJ.M., CasanovasM. and MartínezL., Linguistic aggregation operators for linguistic decision making based on the Dempster-Shafer theory of evidence, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems18 (2010), 287–304.
31.
ChenS.W., LiuJ., WangH., XuY. and AugustoJ.C., A linguistic multi-criteria decision making approach based on logical reasoning, Information Sciences258 (2014), 266–276.
32.
XingY.J. and XingC., Model for evaluating the virtual enterprise’s risk with 2-tuple linguistic information, Journal of Intelligent & Fuzzy Systems31 (2016), 193–200.
33.
PengB., ZhouJ.M. and PengD.H., Cloud model based approach to group decision making with uncertain pure linguistic information, Journal of Intelligent & Fuzzy Systems32 (2017), 1959–1968.
34.
ZhangH.Y., ZhouR., WangJ.Q. and ChenX.H., An FMCDM approach to purchasing decision-making based on cloud model and prospect theory in e-commerce, International Journal of Computational Intelligence Systems9 (2016), 676–688.
35.
WangM.X., WangJ.Q. and LiL., New online personalized recommendation approach based on the perceived value of consumer characteristics, Journal of Intelligent & Fuzzy Systems33 (2017), 1953–1968.
36.
LokeswariY.V. and JacobS.G., A cloud-based data mining framework for improved clinical diagnosis through parallel classification, Applied and Theoretical Computing and Communication Technology (iCATccT), 2015 International Conference on, 2015, pp. 583–588.
37.
WangD., ZengD.B., SinghV.P., XuP.C., LiuD.F., WangY.K., ZengX.K., WuJ.C. and WangL.C., A multidimension cloud model-based approach for water quality assessment, Environmental Research149 (2016), 113–121.
38.
GargH., A novel accuracy function under interval-valued pythagorean fuzzy environment for solving multicriteria decision making problem, Journal of Intelligent & Fuzzy Systems31 (2016), 529–540.
39.
GargH., A novel correlation coefficients between pythagorean fuzzy sets and its applications to decision-making processes, International Journal of Intelligent Systems31 (2016), 1234–1252.
40.
GargH., A new generalized improved score function of interval-valued intuitionistic fuzzy sets and applications in expert systems, Applied Soft Computing38 (2016), 988–999.
41.
GargH., Novel intuitionistic fuzzy decision making method based on an improved operation laws and its application, Engineering Applications of Artificial Intelligence60 (2017), 164–174.
42.
SunR.X., HuJ.H., ZhouJ.D. and ChenX.H., A hesitant fuzzy linguistic projection-based MABAC method for patients’ prioritization, International Journal of Fuzzy Systems (2017). DOI: 10.1007/s40815-017-0345-7.
43.
PawlakZ., Rough sets, International Journal of Parallel Programming11 (1982), 341–356.
44.
XuZ.S., Uncertain linguistic aggregation operators based approach to multiple attribute group decision making under uncertain linguistic environment, Information Sciences168 (2004), 171–184.
45.
LiD.Y., LiuC.Y. and GanW.Y., A new cognitive model: Cloud model, International Journal of Intelligent Systems24 (2009), 357–375.
46.
WangJ.Q., PengL., ZhangH.Y. and ChenX.H., Method of multi-criteria group decision-making based on cloud aggregation operators with linguistic information, Information Sciences274 (2014), 177–191.
47.
Teen-Hang MeenD., ChangT.C. and WangH., A self-testing cloud model for multi-criteria group decision making, Engineering Computations33 (2016), 1767–1783.
48.
WangJ.Q. and YangW.E., Multiple criteria group decision making method based on intuitionistic normal cloud by Monte Carlo simulation, Syst Eng Theory Pract11 (2013), 2859–2865.
49.
MerigóJ.M., CasanovasM. and YangJ.B., Group decision making with expertons and uncertain generalized probabilistic weighted aggregation operators, European Journal of Operational Research235 (2014), 215–224.
50.
YuD.J., MerigóJ.M. and XuY.J., Group decision making in information systems security assessment using dual hesitant fuzzy set, International Journal of Intelligent Systems31 (2016), 786–812.
51.
YuD.J., ZhangW. and XuY.J., Group decision making under hesitant fuzzy environment with application to personnel evaluation, Knowledge-Based Systems52 (2013), 1–10.
YangY., HuJ.H., AnQ.X. and ChenX.H., Group decision making with multiplicative triangular hesitant fuzzy preference relations and cooperative games method, International Journal for Uncertainty Quantification7 (2017), 271–284.
54.
LiangD.C., LiuD. and KobinaA., Three-way group decisions with decision-theoretic rough sets, Information Sciences345 (2016), 46–64.
55.
GargH., Confidence levels based Pythagorean fuzzy aggregation operators and its application to decision-making process, Computational and Mathematical Organization Theory (2017), 1–26. DOI: 10.1007/s10588-017-9242-8.
56.
GargH., Generalized intuitionistic fuzzy interactive geometric interaction operators using Einstein t-norm and t-conorm and their application to decision making, Computers & Industrial Engineering101 (2016), 53–69.
