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Artificial Intelligence Review
Erschienen in: Artificial Intelligence Review 6/2020

21.12.2019

On hesitant neutrosophic rough set over two universes and its application

verfasst von: Hu Zhao, Hong-Ying Zhang

Erschienen in: Artificial Intelligence Review | Ausgabe 6/2020

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Abstract

As a further generalization of the concepts of fuzzy set, intuitionistic fuzzy set, single valued neutrosophic refined set, hesitant fuzzy set, and dual hesitant fuzzy set, Ye (J Intell Syst 24(1):23–36, 2015) proposed the concept of hesitant neutrosophic sets (also called single valued neutrosophic hesitant fuzzy sets). Following the idea of hesitant neutrosophic sets as introduced by Ye, in this paper, the model of hesitant neutrosophic rough sets is proposed, then the join semi-lattice structure of lower and upper hesitant neutrosophic rough approximation operators over two universes is given. In addition, an algorithm to handle decision making problem in medical diagnosis based on hesitant neutrosophic rough sets over two universes is provided. Finally, a numerical example is employed to demonstrate the validness of the proposed hesitant neutrosophic rough sets.
Vorheriger Artikel Sentiment analysis using deep learning architectures: a review
Nächster Artikel A survey of semantic relatedness evaluation datasets and procedures

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Literatur
Atanassov K (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20(1):87–96MATH
Bao YL, Yang HL (2017) On single valued neutrosophic refined rough set model and its applition. J Intell Fuzzy Syst 33(2):1235–1248MathSciNetMATHCrossRef
Biswas P, Pramanik S, Giri BC (2016) GRA method of multiple attribute decision making with single valued neutrosophic hesitant fuzzy set information. In: Smarandache F, Pramanik S (eds) New trends in neutrosophic theory and applications. Pons Editions, Brussels, pp 55–63
Guo Y, Cheng HD (2009) A new neutrosophic approach to image segmentation. Pattern Recognit 42:587–595MATHCrossRef
Guo Y, Sengur A (2015) NCM: neutrosophic c-means clustering algorithm. Pattern Recognit 48(8):2710–2724CrossRef
Khan Q, Mahmood T, Ye J (2017) Multiple attribute decision-making method under hesitant single valued neutrosophic uncertain linguistic environment. J Inequal Spec Funct 8(2):17MathSciNet
Li ZW, Cui RC (2015a) \(T\)-similarity of fuzzy relations and related algebraic structures. Fuzzy Sets Syst 275:130–143MathSciNetMATH
Li ZW, Cui RC (2015b) Similarity of fuzzy relations based on fuzzy topologies induced by fuzzy rough approximation operators. Inf Sci 305:219–233MathSciNetMATHCrossRef
Li ZW, Liu XF, Zhang GQ, NiX Xie, Wang SC (2017) A multi-granulation decision-theoretic rough set method for distributed fc-decision information systems: an application in medical diagnosis. Appl Soft Comput 56:233–244CrossRef
Liu PD, Teng F (2017) Some interval-valued Neutrosophic hesitant fuzzy uncertain linguistic Bonferroni mean aggregation operators and their application in multiple attribute decision making. Int J Uncertain Quantif 7(6):525–572CrossRef
Liu PD, Zhang LL (2017) An extended multiple criteria decision-making method based on neutrosophic hesitant fuzzy information. J Intell Fuzzy Syst 32(6):4403–4413MATHCrossRef
Mahmood T, Ye J, Khan Q (2016) Vector similarity measures for simplified neutrosophic hesitant fuzzy set and their applications. J Inequal Spec Funct 7(4):176–194
Şahin R, Küçük A (2015) Subsethood measure for single valued neutrosophic sets. J Intell Fuzzy Syst 29(2):525–530MATHCrossRef
Salama AA, Broumi S (2014) Roughness of neutrosophic sets. Elixir Appl Math 74:26833–26837
Smarandache F (1998) Neutrosophy: neutrosophic probability, set, and logic. American Research Press, RehobothMATH
Smarandache F (1999) A unifying field in logics. neutrosophy: neutrosophic probability, set and logic. American Research Press, RehobothMATH
Smarandache F (2002) A unifying field in logics: neutrosophic logic. Int J Mult Valued Log 8(3): 385–438, ISSN: 1023–6627
Torra V (2010) Hesitant fuzzy sets. Int J Intell Syst 25:529–539MATH
Torra V, Narukawa Y (2009) on hesitant fuzzy sets and decision. In: The 18th IEEE international conference on fuzzy systems. Jeju Island, pp 1378–1382
Xu ZS, Xia MM (2011) Distance and similarity measures for hesitant fuzy sets. inform. Sciences 181:2128–2138MATH
Yang HL, Guo ZL, She YH, Liao XW (2016) On single valued neutrosophic relations. J Intell Fuzzy Syst 30:1045–1056MATHCrossRef
Yang HL, Zhang CL, Guo ZL, Liu YL, Liao XW (2017) A hybrid model of single valued neutrosophic sets and rough sets: single valued neutrosophic rough set model. Soft Comput 21(21):6253–6267MATHCrossRef
Ye J (2013) Multicriteria decision-making method using the correlation coefficient under single-valued neutrosophic environment. Int J Gen Syst 42(4):386–394MathSciNetMATHCrossRef
Ye J (2014) Improved correlation coefficients of single valued neutrosophic sets and interval neutrosophic sets for multiple attribute decision making. J Intell Fuzzy Syst 27:2453–2462MATHCrossRef
Ye J (2015) Multiple-attribute decision-making method using the under a single-valued neutrosophic hesitant fuzzy environment. J Intell Syst 24(1):23–36CrossRef
Ye J (2016) Correlation coefficients of interval neutrosophic hesitant fuzzy sets and its application in a multiple attribute decision making method. Informatica 27(1):179–202MATHCrossRef
Ye J (2018) Multiple-attribute decision-making method using similarity measures of single-valued neutrosophic hesitant fuzzy sets based on least common multiple cardinality. J Intell Fuzzy Syst 34(6):4203–4211CrossRef
Ye S, Ye J (2014) Dice similarity measure between single valued neutrosophic multisets and its applcation in medical diagnosis. Neutrosophic Sets Syst 6:48–53
Zhang HD, Shu L, Liao SL (2017a) Hesitant fuzzy rough set over two universes and its application in decision making. Soft Comput 21(7):1803–1816MATHCrossRef
Zhang XH, Bo CX, Smarandache F, Dai JH (2018a) New inclusion relation of neutrosophic sets with applications and related lattice structure. Int J Mach Learn Cybern 9:1753–1763CrossRef
Zhao H, Zhang HY (2018a) A result on single valued neutrosophic refined rough approximation operators. J Intell Fuzzy Syst 35:3139–3146CrossRef
Metadaten
Titel
On hesitant neutrosophic rough set over two universes and its application
verfasst von
Hu Zhao
Hong-Ying Zhang
Publikationsdatum
21.12.2019
Verlag
Springer Netherlands
Erschienen in
Artificial Intelligence Review / Ausgabe 6/2020
Print ISSN: 0269-2821
Elektronische ISSN: 1573-7462
DOI
https://doi.org/10.1007/s10462-019-09795-4

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