[1] Li T R, Chen H M, Yao J A, et al. A multifaceted analysis of probabilistic three-way decisions [J]. Fundamenta Informaticae, 2014, 132(3): 291-313. DOI:10.3233/FI-2014-1045.
[2] Deng X F, Yao Y Y. Decision-theoretic three-way approximations of fuzzy sets [J]. Information Sciences, 2014, 279: 702-715. DOI:10.1016/j.ins.2014.04.022.
[3] Yao Y Y. Three-way decisions with probabilistic rough sets [J]. Information Sciences, 2010, 180(3): 341-353. DOI:10.1016/j.ins.2009.09.021.
[4] Hu B Q. Three-way decisions space and three-way decisions [J]. Information Sciences, 2014, 281: 21-52. DOI:10.1016/j.ins.2014.05.015.
[5] Xu Z S. Linguistic decision making: Theory and methods [M]. Berlin: Springer, 2012.
[6] Ma J, Ruan D, Xu Y, et al. A fuzzy-set approach to treat determinacy and consistency of linguistic terms in multi-criteria decision making [J]. International Journal of Approximate Reasoning, 2007, 44(2): 165-181.
[7] Herrera F, Herrera-Viedma E, Martinez L. A fuzzy linguistic methodology to deal with unbalanced linguistic term sets [J]. IEEE Transactions on Fuzzy Systems, 2008, 16(2): 354-370. DOI:10.1109/tfuzz.2007.896353.
[8] Massanet S, Riera J V, Torrens J, et al. A new linguistic computational model based on discrete fuzzy numbers for computing with words [J]. Information Sciences, 2014, 258: 277-290. DOI:10.1016/j.ins.2013.06.055.
[9] Liang D C, Liu D. Three-way decisions based on decision-theoretic rough sets under linguistic assessment with the aid of group decision making [J]. Applied Soft Computing, 2015, 29: 256-269. DOI:10.1016/j.asoc.2015.01.008.
[10] Mendel J M, Wu D R. Peceptual computing: Aiding people in making subjective judgments [M]. New Jersey: John Wiley and Sons, Inc., 2010.
[11] Yang, X J, Yan L L, Peng H, et al. Encoding words into Cloud models from interval-valued data via fuzzy statistics and membership function fitting [J]. Knowledge-Based Systems, 2014, 55: 114-124. DOI:10.1016/j.knosys.2013.10.014.
[12] Meng F Y, Chen X H, Zhang Q. Multi-attribute decision analysis under a linguistic hesitant fuzzy environment [J]. Information Sciences, 2014, 267: 287-305. DOI:10.1016/j.ins.2014.02.012.
[13] Kacprzyk J, Zadrozny S. Computing with words is an implementable paradigm: Fuzzy queries, linguistic data summaries, and natural-language generation [J]. IEEE Transactions on Fuzzy Systems, 2010, 18(3): 461-472. DOI:10.1109/tfuzz.2010.2040480.
[14] Cao Y Z, Chen G Q. A fuzzy Petri-nets model for computing with words [J]. IEEE Transactions on Fuzzy Systems, 2010, 18(3): 486-499. DOI:10.1109/tfuzz.2009.2035816.
[15] Wang J H, Hao J Y. A new version of 2-tuple fuzzy linguistic, representation model for computing with words [J]. IEEE Transactions on Fuzzy Systems, 2006, 14(3): 435-445.
[16] Liu F, Mendel J M. Encoding words into interval type-2 fuzzy sets using an interval approach [J]. IEEE Transactions on Fuzzy Systems, 2008, 16(6): 1503-1521.
[17] Qin J D, Liu X W. Multi-attribute group decision making using combined ranking value under interval type-2 fuzzy environment [J]. Information Sciences, 2015, 297: 293-315. DOI:10.1016/j.ins.2014.11.022.
[18] Qin J D, Liu X W, Pedrycz W. An extended VIKOR method based on prospect theory for multiple attribute decision making under interval type-2 fuzzy environment [J]. Knowledge-Based Systems, 2015, 86: 116-130. DOI:10.1016/j.knosys.2015.05.025.