Relative measure-based approaches for ranking single-valued neutrosophic values and their applications

Abstract During uncertain information processing on generalized fuzzy values, how to rank two single-valued neutrosophic values is an important and omnipresent issue in all kinds of intelligent decision problems solving. Although many orders have been proposed to compare any two single-valued neutro...
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Autor*in:	Huang, Bing [verfasserIn] Yang, Xuan Feng, Guofu Guo, Chunxiang

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Distance Relative measure Order Similarity degree Single-valued neutrosophic set Single-valued neutrosophic value

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021

Übergeordnetes Werk:	Enthalten in: International journal of machine learning and cybernetics - Heidelberg : Springer, 2010, 13(2021), 6 vom: 18. Nov., Seite 1535-1552
Übergeordnetes Werk:	volume:13 ; year:2021 ; number:6 ; day:18 ; month:11 ; pages:1535-1552

Links:	Volltext

DOI / URN:	10.1007/s13042-021-01464-9

Katalog-ID:	SPR046841245

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520			\|a Abstract During uncertain information processing on generalized fuzzy values, how to rank two single-valued neutrosophic values is an important and omnipresent issue in all kinds of intelligent decision problems solving. Although many orders have been proposed to compare any two single-valued neutrosophic values, some shortcomings may exist when they are utilized. Inspired by the Euclidean approach for ranking intuitionistic fuzzy values, we present two types of orders by using the notion of relative geometric distance and relative similarity degree, respectively. First, we present two relative distance-based and relative similarity-based measures to describe the favorable degree of the single-valued neutrosophic value by considering three distances and similarity degrees between a single-valued neutrosophic value and the ideal negative point, ideal positive point, and most uncertain point. Second, two orders over the set of all single-valued neutrosophic values and the corresponding ranking methods for single-valued neutrosophic sets are devised on the basis of the presented measures of an single-valued neutrosophic value, and their properties are discussed. Third, we extend the presented ranking method for single-valued neutrosophic values and single-valued neutrosophic sets by introducing human attitudes using different weights. Finally, we apply the presented methods to optimal alternative selection and group decision making and obtain the effective and reasonable results. The main thoughts of this study can be applied in various generalized fuzzy decision problem solving.
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allfields	10.1007/s13042-021-01464-9 doi (DE-627)SPR046841245 (SPR)s13042-021-01464-9-e DE-627 ger DE-627 rakwb eng Huang, Bing verfasserin aut Relative measure-based approaches for ranking single-valued neutrosophic values and their applications 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract During uncertain information processing on generalized fuzzy values, how to rank two single-valued neutrosophic values is an important and omnipresent issue in all kinds of intelligent decision problems solving. Although many orders have been proposed to compare any two single-valued neutrosophic values, some shortcomings may exist when they are utilized. Inspired by the Euclidean approach for ranking intuitionistic fuzzy values, we present two types of orders by using the notion of relative geometric distance and relative similarity degree, respectively. First, we present two relative distance-based and relative similarity-based measures to describe the favorable degree of the single-valued neutrosophic value by considering three distances and similarity degrees between a single-valued neutrosophic value and the ideal negative point, ideal positive point, and most uncertain point. Second, two orders over the set of all single-valued neutrosophic values and the corresponding ranking methods for single-valued neutrosophic sets are devised on the basis of the presented measures of an single-valued neutrosophic value, and their properties are discussed. Third, we extend the presented ranking method for single-valued neutrosophic values and single-valued neutrosophic sets by introducing human attitudes using different weights. Finally, we apply the presented methods to optimal alternative selection and group decision making and obtain the effective and reasonable results. The main thoughts of this study can be applied in various generalized fuzzy decision problem solving. Distance (dpeaa)DE-He213 Relative measure (dpeaa)DE-He213 Order (dpeaa)DE-He213 Similarity degree (dpeaa)DE-He213 Single-valued neutrosophic set (dpeaa)DE-He213 Single-valued neutrosophic value (dpeaa)DE-He213 Yang, Xuan aut Feng, Guofu aut Guo, Chunxiang aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 13(2021), 6 vom: 18. Nov., Seite 1535-1552 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:13 year:2021 number:6 day:18 month:11 pages:1535-1552 https://dx.doi.org/10.1007/s13042-021-01464-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 13 2021 6 18 11 1535-1552
spelling	10.1007/s13042-021-01464-9 doi (DE-627)SPR046841245 (SPR)s13042-021-01464-9-e DE-627 ger DE-627 rakwb eng Huang, Bing verfasserin aut Relative measure-based approaches for ranking single-valued neutrosophic values and their applications 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract During uncertain information processing on generalized fuzzy values, how to rank two single-valued neutrosophic values is an important and omnipresent issue in all kinds of intelligent decision problems solving. Although many orders have been proposed to compare any two single-valued neutrosophic values, some shortcomings may exist when they are utilized. Inspired by the Euclidean approach for ranking intuitionistic fuzzy values, we present two types of orders by using the notion of relative geometric distance and relative similarity degree, respectively. First, we present two relative distance-based and relative similarity-based measures to describe the favorable degree of the single-valued neutrosophic value by considering three distances and similarity degrees between a single-valued neutrosophic value and the ideal negative point, ideal positive point, and most uncertain point. Second, two orders over the set of all single-valued neutrosophic values and the corresponding ranking methods for single-valued neutrosophic sets are devised on the basis of the presented measures of an single-valued neutrosophic value, and their properties are discussed. Third, we extend the presented ranking method for single-valued neutrosophic values and single-valued neutrosophic sets by introducing human attitudes using different weights. Finally, we apply the presented methods to optimal alternative selection and group decision making and obtain the effective and reasonable results. The main thoughts of this study can be applied in various generalized fuzzy decision problem solving. Distance (dpeaa)DE-He213 Relative measure (dpeaa)DE-He213 Order (dpeaa)DE-He213 Similarity degree (dpeaa)DE-He213 Single-valued neutrosophic set (dpeaa)DE-He213 Single-valued neutrosophic value (dpeaa)DE-He213 Yang, Xuan aut Feng, Guofu aut Guo, Chunxiang aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 13(2021), 6 vom: 18. Nov., Seite 1535-1552 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:13 year:2021 number:6 day:18 month:11 pages:1535-1552 https://dx.doi.org/10.1007/s13042-021-01464-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 13 2021 6 18 11 1535-1552
allfields_unstemmed	10.1007/s13042-021-01464-9 doi (DE-627)SPR046841245 (SPR)s13042-021-01464-9-e DE-627 ger DE-627 rakwb eng Huang, Bing verfasserin aut Relative measure-based approaches for ranking single-valued neutrosophic values and their applications 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract During uncertain information processing on generalized fuzzy values, how to rank two single-valued neutrosophic values is an important and omnipresent issue in all kinds of intelligent decision problems solving. Although many orders have been proposed to compare any two single-valued neutrosophic values, some shortcomings may exist when they are utilized. Inspired by the Euclidean approach for ranking intuitionistic fuzzy values, we present two types of orders by using the notion of relative geometric distance and relative similarity degree, respectively. First, we present two relative distance-based and relative similarity-based measures to describe the favorable degree of the single-valued neutrosophic value by considering three distances and similarity degrees between a single-valued neutrosophic value and the ideal negative point, ideal positive point, and most uncertain point. Second, two orders over the set of all single-valued neutrosophic values and the corresponding ranking methods for single-valued neutrosophic sets are devised on the basis of the presented measures of an single-valued neutrosophic value, and their properties are discussed. Third, we extend the presented ranking method for single-valued neutrosophic values and single-valued neutrosophic sets by introducing human attitudes using different weights. Finally, we apply the presented methods to optimal alternative selection and group decision making and obtain the effective and reasonable results. The main thoughts of this study can be applied in various generalized fuzzy decision problem solving. Distance (dpeaa)DE-He213 Relative measure (dpeaa)DE-He213 Order (dpeaa)DE-He213 Similarity degree (dpeaa)DE-He213 Single-valued neutrosophic set (dpeaa)DE-He213 Single-valued neutrosophic value (dpeaa)DE-He213 Yang, Xuan aut Feng, Guofu aut Guo, Chunxiang aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 13(2021), 6 vom: 18. Nov., Seite 1535-1552 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:13 year:2021 number:6 day:18 month:11 pages:1535-1552 https://dx.doi.org/10.1007/s13042-021-01464-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 13 2021 6 18 11 1535-1552
allfieldsGer	10.1007/s13042-021-01464-9 doi (DE-627)SPR046841245 (SPR)s13042-021-01464-9-e DE-627 ger DE-627 rakwb eng Huang, Bing verfasserin aut Relative measure-based approaches for ranking single-valued neutrosophic values and their applications 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract During uncertain information processing on generalized fuzzy values, how to rank two single-valued neutrosophic values is an important and omnipresent issue in all kinds of intelligent decision problems solving. Although many orders have been proposed to compare any two single-valued neutrosophic values, some shortcomings may exist when they are utilized. Inspired by the Euclidean approach for ranking intuitionistic fuzzy values, we present two types of orders by using the notion of relative geometric distance and relative similarity degree, respectively. First, we present two relative distance-based and relative similarity-based measures to describe the favorable degree of the single-valued neutrosophic value by considering three distances and similarity degrees between a single-valued neutrosophic value and the ideal negative point, ideal positive point, and most uncertain point. Second, two orders over the set of all single-valued neutrosophic values and the corresponding ranking methods for single-valued neutrosophic sets are devised on the basis of the presented measures of an single-valued neutrosophic value, and their properties are discussed. Third, we extend the presented ranking method for single-valued neutrosophic values and single-valued neutrosophic sets by introducing human attitudes using different weights. Finally, we apply the presented methods to optimal alternative selection and group decision making and obtain the effective and reasonable results. The main thoughts of this study can be applied in various generalized fuzzy decision problem solving. Distance (dpeaa)DE-He213 Relative measure (dpeaa)DE-He213 Order (dpeaa)DE-He213 Similarity degree (dpeaa)DE-He213 Single-valued neutrosophic set (dpeaa)DE-He213 Single-valued neutrosophic value (dpeaa)DE-He213 Yang, Xuan aut Feng, Guofu aut Guo, Chunxiang aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 13(2021), 6 vom: 18. Nov., Seite 1535-1552 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:13 year:2021 number:6 day:18 month:11 pages:1535-1552 https://dx.doi.org/10.1007/s13042-021-01464-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 13 2021 6 18 11 1535-1552
allfieldsSound	10.1007/s13042-021-01464-9 doi (DE-627)SPR046841245 (SPR)s13042-021-01464-9-e DE-627 ger DE-627 rakwb eng Huang, Bing verfasserin aut Relative measure-based approaches for ranking single-valued neutrosophic values and their applications 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract During uncertain information processing on generalized fuzzy values, how to rank two single-valued neutrosophic values is an important and omnipresent issue in all kinds of intelligent decision problems solving. Although many orders have been proposed to compare any two single-valued neutrosophic values, some shortcomings may exist when they are utilized. Inspired by the Euclidean approach for ranking intuitionistic fuzzy values, we present two types of orders by using the notion of relative geometric distance and relative similarity degree, respectively. First, we present two relative distance-based and relative similarity-based measures to describe the favorable degree of the single-valued neutrosophic value by considering three distances and similarity degrees between a single-valued neutrosophic value and the ideal negative point, ideal positive point, and most uncertain point. Second, two orders over the set of all single-valued neutrosophic values and the corresponding ranking methods for single-valued neutrosophic sets are devised on the basis of the presented measures of an single-valued neutrosophic value, and their properties are discussed. Third, we extend the presented ranking method for single-valued neutrosophic values and single-valued neutrosophic sets by introducing human attitudes using different weights. Finally, we apply the presented methods to optimal alternative selection and group decision making and obtain the effective and reasonable results. The main thoughts of this study can be applied in various generalized fuzzy decision problem solving. Distance (dpeaa)DE-He213 Relative measure (dpeaa)DE-He213 Order (dpeaa)DE-He213 Similarity degree (dpeaa)DE-He213 Single-valued neutrosophic set (dpeaa)DE-He213 Single-valued neutrosophic value (dpeaa)DE-He213 Yang, Xuan aut Feng, Guofu aut Guo, Chunxiang aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 13(2021), 6 vom: 18. Nov., Seite 1535-1552 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:13 year:2021 number:6 day:18 month:11 pages:1535-1552 https://dx.doi.org/10.1007/s13042-021-01464-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 13 2021 6 18 11 1535-1552
language	English
source	Enthalten in International journal of machine learning and cybernetics 13(2021), 6 vom: 18. Nov., Seite 1535-1552 volume:13 year:2021 number:6 day:18 month:11 pages:1535-1552
sourceStr	Enthalten in International journal of machine learning and cybernetics 13(2021), 6 vom: 18. Nov., Seite 1535-1552 volume:13 year:2021 number:6 day:18 month:11 pages:1535-1552
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Distance Relative measure Order Similarity degree Single-valued neutrosophic set Single-valued neutrosophic value
isfreeaccess_bool	false
container_title	International journal of machine learning and cybernetics
authorswithroles_txt_mv	Huang, Bing @@aut@@ Yang, Xuan @@aut@@ Feng, Guofu @@aut@@ Guo, Chunxiang @@aut@@
publishDateDaySort_date	2021-11-18T00:00:00Z
hierarchy_top_id	635135132
id	SPR046841245
language_de	englisch
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author	Huang, Bing
spellingShingle	Huang, Bing misc Distance misc Relative measure misc Order misc Similarity degree misc Single-valued neutrosophic set misc Single-valued neutrosophic value Relative measure-based approaches for ranking single-valued neutrosophic values and their applications
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topic_title	Relative measure-based approaches for ranking single-valued neutrosophic values and their applications Distance (dpeaa)DE-He213 Relative measure (dpeaa)DE-He213 Order (dpeaa)DE-He213 Similarity degree (dpeaa)DE-He213 Single-valued neutrosophic set (dpeaa)DE-He213 Single-valued neutrosophic value (dpeaa)DE-He213
topic	misc Distance misc Relative measure misc Order misc Similarity degree misc Single-valued neutrosophic set misc Single-valued neutrosophic value
topic_unstemmed	misc Distance misc Relative measure misc Order misc Similarity degree misc Single-valued neutrosophic set misc Single-valued neutrosophic value
topic_browse	misc Distance misc Relative measure misc Order misc Similarity degree misc Single-valued neutrosophic set misc Single-valued neutrosophic value
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title	Relative measure-based approaches for ranking single-valued neutrosophic values and their applications
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title_full	Relative measure-based approaches for ranking single-valued neutrosophic values and their applications
author_sort	Huang, Bing
journal	International journal of machine learning and cybernetics
journalStr	International journal of machine learning and cybernetics
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author_browse	Huang, Bing Yang, Xuan Feng, Guofu Guo, Chunxiang
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doi_str_mv	10.1007/s13042-021-01464-9
title_sort	relative measure-based approaches for ranking single-valued neutrosophic values and their applications
title_auth	Relative measure-based approaches for ranking single-valued neutrosophic values and their applications
abstract	Abstract During uncertain information processing on generalized fuzzy values, how to rank two single-valued neutrosophic values is an important and omnipresent issue in all kinds of intelligent decision problems solving. Although many orders have been proposed to compare any two single-valued neutrosophic values, some shortcomings may exist when they are utilized. Inspired by the Euclidean approach for ranking intuitionistic fuzzy values, we present two types of orders by using the notion of relative geometric distance and relative similarity degree, respectively. First, we present two relative distance-based and relative similarity-based measures to describe the favorable degree of the single-valued neutrosophic value by considering three distances and similarity degrees between a single-valued neutrosophic value and the ideal negative point, ideal positive point, and most uncertain point. Second, two orders over the set of all single-valued neutrosophic values and the corresponding ranking methods for single-valued neutrosophic sets are devised on the basis of the presented measures of an single-valued neutrosophic value, and their properties are discussed. Third, we extend the presented ranking method for single-valued neutrosophic values and single-valued neutrosophic sets by introducing human attitudes using different weights. Finally, we apply the presented methods to optimal alternative selection and group decision making and obtain the effective and reasonable results. The main thoughts of this study can be applied in various generalized fuzzy decision problem solving. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021
abstractGer	Abstract During uncertain information processing on generalized fuzzy values, how to rank two single-valued neutrosophic values is an important and omnipresent issue in all kinds of intelligent decision problems solving. Although many orders have been proposed to compare any two single-valued neutrosophic values, some shortcomings may exist when they are utilized. Inspired by the Euclidean approach for ranking intuitionistic fuzzy values, we present two types of orders by using the notion of relative geometric distance and relative similarity degree, respectively. First, we present two relative distance-based and relative similarity-based measures to describe the favorable degree of the single-valued neutrosophic value by considering three distances and similarity degrees between a single-valued neutrosophic value and the ideal negative point, ideal positive point, and most uncertain point. Second, two orders over the set of all single-valued neutrosophic values and the corresponding ranking methods for single-valued neutrosophic sets are devised on the basis of the presented measures of an single-valued neutrosophic value, and their properties are discussed. Third, we extend the presented ranking method for single-valued neutrosophic values and single-valued neutrosophic sets by introducing human attitudes using different weights. Finally, we apply the presented methods to optimal alternative selection and group decision making and obtain the effective and reasonable results. The main thoughts of this study can be applied in various generalized fuzzy decision problem solving. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021
abstract_unstemmed	Abstract During uncertain information processing on generalized fuzzy values, how to rank two single-valued neutrosophic values is an important and omnipresent issue in all kinds of intelligent decision problems solving. Although many orders have been proposed to compare any two single-valued neutrosophic values, some shortcomings may exist when they are utilized. Inspired by the Euclidean approach for ranking intuitionistic fuzzy values, we present two types of orders by using the notion of relative geometric distance and relative similarity degree, respectively. First, we present two relative distance-based and relative similarity-based measures to describe the favorable degree of the single-valued neutrosophic value by considering three distances and similarity degrees between a single-valued neutrosophic value and the ideal negative point, ideal positive point, and most uncertain point. Second, two orders over the set of all single-valued neutrosophic values and the corresponding ranking methods for single-valued neutrosophic sets are devised on the basis of the presented measures of an single-valued neutrosophic value, and their properties are discussed. Third, we extend the presented ranking method for single-valued neutrosophic values and single-valued neutrosophic sets by introducing human attitudes using different weights. Finally, we apply the presented methods to optimal alternative selection and group decision making and obtain the effective and reasonable results. The main thoughts of this study can be applied in various generalized fuzzy decision problem solving. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021
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title_short	Relative measure-based approaches for ranking single-valued neutrosophic values and their applications
url	https://dx.doi.org/10.1007/s13042-021-01464-9
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author2	Yang, Xuan Feng, Guofu Guo, Chunxiang
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up_date	2024-07-04T00:39:59.282Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR046841245</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230507164359.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220426s2021 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s13042-021-01464-9</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR046841245</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s13042-021-01464-9-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Huang, Bing</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Relative measure-based approaches for ranking single-valued neutrosophic values and their applications</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract During uncertain information processing on generalized fuzzy values, how to rank two single-valued neutrosophic values is an important and omnipresent issue in all kinds of intelligent decision problems solving. Although many orders have been proposed to compare any two single-valued neutrosophic values, some shortcomings may exist when they are utilized. Inspired by the Euclidean approach for ranking intuitionistic fuzzy values, we present two types of orders by using the notion of relative geometric distance and relative similarity degree, respectively. First, we present two relative distance-based and relative similarity-based measures to describe the favorable degree of the single-valued neutrosophic value by considering three distances and similarity degrees between a single-valued neutrosophic value and the ideal negative point, ideal positive point, and most uncertain point. Second, two orders over the set of all single-valued neutrosophic values and the corresponding ranking methods for single-valued neutrosophic sets are devised on the basis of the presented measures of an single-valued neutrosophic value, and their properties are discussed. Third, we extend the presented ranking method for single-valued neutrosophic values and single-valued neutrosophic sets by introducing human attitudes using different weights. Finally, we apply the presented methods to optimal alternative selection and group decision making and obtain the effective and reasonable results. The main thoughts of this study can be applied in various generalized fuzzy decision problem solving.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Distance</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Relative measure</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Order</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Similarity degree</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Single-valued neutrosophic set</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Single-valued neutrosophic value</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yang, Xuan</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Feng, Guofu</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Guo, Chunxiang</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of machine learning and cybernetics</subfield><subfield code="d">Heidelberg : Springer, 2010</subfield><subfield code="g">13(2021), 6 vom: 18. Nov., Seite 1535-1552</subfield><subfield code="w">(DE-627)635135132</subfield><subfield code="w">(DE-600)2572473-3</subfield><subfield code="x">1868-808X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:13</subfield><subfield code="g">year:2021</subfield><subfield code="g">number:6</subfield><subfield code="g">day:18</subfield><subfield code="g">month:11</subfield><subfield code="g">pages:1535-1552</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s13042-021-01464-9</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield 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