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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 4/2018

05.05.2016 | Original Article

Hesitant fuzzy linguistic rough set over two universes model and its applications

verfasst von: Chao Zhang, Deyu Li, Jiye Liang

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 4/2018

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Abstract

In practical decision making situations, decision makers usually express preferences by evaluating qualitative linguistic alternatives using the hesitant fuzzy linguistic term set. To analyze the hesitant fuzzy linguistic information effectively, we aim to apply the rough set over two universes model. Thus, it is necessary to study the fusion of the hesitant fuzzy linguistic term set and rough set over two universes. This paper proposes a general framework for the study of the hesitant fuzzy linguistic rough set over two universes. First, both the definitions and some fundamental properties will be developed, followed by construction of a general decision making rule based on the hesitant fuzzy linguistic information. Finally, we illustrate the newly proposed approach according to the basis of person-job fit, and discuss its applications compared to classical methods.
Vorheriger Artikel Generalized linear discriminant analysis based on euclidean norm for gait recognition
Nächster Artikel Association rule mining using treap

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Literatur
1.
2.
3.
Caldwell DF, O’Reilly CA (1990) Measuring person-job fit with a profile-comparison process. J Appl Psychol 75(6):648CrossRef
4.
Deepak D, John SJ (2014) Hesitant fuzzy rough sets through hesitant fuzzy relations. Ann Fuzzy Math Inform 8(1):33–46MathSciNetMATH
5.
Dubois D, Prade H (1990) Rough fuzzy sets and fuzzy rough sets. Int J Gen Syst 17(2–3):191–209CrossRefMATH
6.
Greco S, Matarazzo B, Slowinski R (2002) Rough approximation by dominance relations. Int J Intell Syst 17(2):153–171CrossRefMATH
7.
Greco S, Słowiński R, Zielniewicz P (2013) Putting dominance-based rough set approach and robust ordinal regression together. Decis Support Syst 54(2):891–903CrossRef
8.
9.
Hu Q, Yu D, Xie Z (2008) Neighborhood classifiers. Expert Syst Appl 34(2):866–876CrossRef
10.
Huang H, Yang X (2014) Pairwise comparison and distance measure of hesitant fuzzy linguistic term sets. Math Probl Eng 21(3):166–183MathSciNet
11.
12.
Kraft DH, Bordogna G, Pasi G (1999) Fuzzy set techniques in information retrieval. In: Fuzzy Sets in Approximate Reasoning and Information Systems, Springer, pp 469–510
13.
Lauver KJ, Kristof BA (2001) Distinguishing between employees’ perceptions of person-job and person-organization fit. J Vocat Behav 59(3):454–470CrossRef
14.
Li D, Zhang B, Leung Y (2004) On knowledge reduction in inconsistent decision information systems. Int J Uncert Fuzz Know-Based Syst 12(05):651–672MathSciNetCrossRefMATH
15.
Liang D, Liu D (2015) A novel risk decision making based on decision-theoretic rough sets under hesitant fuzzy information. Fuzzy Syst IEEE Trans 23(2):237–247CrossRef
16.
Liang J, Wang F, Dang C, Qian Y (2012) An efficient rough feature selection algorithm with a multi-granulation view. Int J Approx Reason 53(6):912–926MathSciNetCrossRef
17.
Liao H, Xu Z (2015) Approaches to manage hesitant fuzzy linguistic information based on the cosine distance and similarity measures for hfltss and their application in qualitative decision making. Expert Syst Appl 42(12):5328–5336CrossRef
18.
Lu S, Wang X, Zhang G, Zhou X (2015) Effective algorithms of the moore-penrose inverse matrices for extreme learning machine. Intell Data Anal 19(4):743–760CrossRef
19.
Mendel JM, John RIB (2002) Type-2 fuzzy sets made simple. Fuzzy Syst IEEE Trans 10(2):117–127CrossRef
20.
21.
22.
23.
Peng Y, Mao C (2015) The impact of person-job fit on job satisfaction: The mediator role of self efficacy. Soc Indic Res 121(3):805–813CrossRef
24.
25.
Rodríguez RM, Martínez L, Herrera F (2012) Hesitant fuzzy linguistic term sets for decision making. Fuzzy Syst IEEE Trans 20(1):109–119CrossRef
26.
Rodríguez RM, Martínez L, Herrera F (2013) A group decision making model dealing with comparative linguistic expressions based on hesitant fuzzy linguistic term sets. Inf Sci 241:28–42MathSciNetCrossRefMATH
27.
Sun B, Ma W, Zhao H (2013) A fuzzy rough set approach to emergency material demand prediction over two universes. Appl Math Model 37(10):7062–7070MathSciNetCrossRef
28.
Torra V (2010) Hesitant fuzzy sets. Int J Intell Syst 25(6):529–539MATH
29.
Torra V, Narukawa Y (2009) On hesitant fuzzy sets and decision. In: Fuzzy Systems, 2009. FUZZ-IEEE 2009. IEEE International Conference on, IEEE, pp 1378–1382
30.
31.
Wang X, Ashfaq RAR, Fu A (2015) Fuzziness based sample categorization for classifier performance improvement. J Intell Fuzzy Syst 29(3):1185–1196MathSciNetCrossRef
32.
Wei C, Zhao N, Tang X (2014) Operators and comparisons of hesitant fuzzy linguistic term sets. Fuzzy Syst IEEE Trans 22(3):575–585CrossRef
33.
Xu W, Sun W, Liu Y, Zhang W (2013) Fuzzy rough set models over two universes. Int J Mach Learn Cyber 4(6):631–645CrossRef
34.
Xu Y, Xu A, Wang H (2015) Hesitant fuzzy linguistic linear programming technique for multidimensional analysis of preference for multi-attribute group decision making. Int J Mach Learn Cyber pp 1–11
35.
36.
Yang X, Song X, Qi Y, Yang J (2014) Constructive and axiomatic approaches to hesitant fuzzy rough set. Soft Comput 18(6):1067–1077CrossRefMATH
37.
Yeung DS, Chen D, Tsang EC, Lee JW, Wang X (2005) On the generalization of fuzzy rough sets. Fuzzy Syst IEEE Trans 13(3):343–361CrossRef
38.
39.
Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning–i. Inf Sci 8(3):199–249MathSciNetCrossRefMATH
40.
Zhang C, Li D, Yan Y (2015) A dual hesitant fuzzy multigranulation rough set over two-universe model for medical diagnoses. Comp Math Methods Med 2015:1–12MathSciNetMATH
41.
Zhang C, Li D, Ren R (2016) Pythagorean fuzzy multigranulation rough set over two universes and its applications in merger and acquisition. Int J Intell Syst. doi:10.​1002/​int.​21811
42.
Zhang H, Shu L, Liao S (2014) On interval-valued hesitant fuzzy rough approximation operators. Soft Computing pp 1–21
43.
Zhu B, Xu Z (2014) Consistency measures for hesitant fuzzy linguistic preference relations. Fuzzy Syst IEEE Trans 22(1):35–45CrossRef
Metadaten
Titel
Hesitant fuzzy linguistic rough set over two universes model and its applications
verfasst von
Chao Zhang
Deyu Li
Jiye Liang
Publikationsdatum
05.05.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 4/2018
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-016-0541-z

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