Research Article
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New Correlation Coefficients between Linguistic Neutrosophic Numbers and Their Group Decision Making Method

Year 2019, Issue: 28, 74 - 83, 07.05.2019

Abstract

Since linguistic neutrosophic
numbers (LNNs) are depicted independently by the truth, indeterminacy, and
falsity linguistic variables in indeterminate and inconsistent linguistic
environment, they are very fit for human thinking and expressing habits to
judgments of complex objects in real life world. Then the correlation
coefficient is a critical mathematical tool in pattern recognition and decision
making science, but the related research was rarely involved in LNN setting.
Hence, this work first proposes two new correlation coefficients of LNNs based
on the correlation and information energy of LNNs as the complement/extension
of our previous work, and then develops a multiple criteria
group decision making (MCGDM) method based on the proposed correlation coefficients
in LNN setting.
Lastly,
a decision making example is provided to illustrate the applicability of the
developed method. By comparison with the MCGDM methods regarding the existing
correlation coefficients based on the maximum and minimum operations of LNNs,
the decision results indicate the effectiveness of the developed MCGDM
approach. Hence, the proposed approach provides another new way for linguistic
neutrosophic decision making problems.

Supporting Institution

National Natural Science Foundation of China

Project Number

61703280

References

  • F. Smarandache, Neutrosophy: Neutrosophic probability, set, and logic. American Research Press, Rehoboth, USA, 1998.
  • Y. H. Guo, C. Zhou, H. P. Chan, A. Chughtai, J. Wei, L. M. Hadjiiski, E. A. Kazerooni, Automated iterative neutrosophic lung segmentation for image analysis in thoracic computed tomography. Medical Physics 40 (2013) 081912.
  • Y. H. Guo, A. Sengur, J. W. Tian, A novel breast ultrasound image segmentation algorithm based on neutrosophic similarity score and level set. Computer Methods and Programs in Biomedicine 123 (2016) 43–53.
  • K. M. Amin, A. I. Shahin, Y. H. Guo, A novel breast tumor classification algorithm using neutrosophic score features. Measurement 81 (2016) 210–220.
  • J. Ye, Improved cosine similarity measures of simplified neutrosophic sets for medical diagnoses. Artificial Intelligence in Medicine 63(3) (2015) 171–179.
  • J. Ye, J. Fu, Multi-period medical diagnosis method using a single valued neutrosophic similarity measure based on tangent function, Computer Methods and Programs in Biomedicine 123 (2016) 142-149.
  • J. Fu, J. Ye, Simplified neutrosophic exponential similarity measures for the initial evaluation/diagnosis of benign prostatic hyperplasia symptom, Symmetry 9(8) (2017) 154.
  • J. Ye, Fault diagnoses of steam turbine using the exponential similarity measure of neutrosophic numbers, Journal of Intelligent & Fuzzy Systems 30 (2016) 1927–1934.
  • J. Ye, Single valued neutrosophic similarity measures based on cotangent function and their application in the fault diagnosis of steam turbine, Soft Computing 21(3) (2017) 817-825.
  • J. Ye, Fault diagnoses of hydraulic turbine using the dimension root similarity measure of single-valued neutrosophic sets. Intelligent Automation & Soft Computing 24(1) (2018) 1-8.
  • J. Ye, Multicriteria decision-making method using the correlation coefficient under single-valued neutrosophic environment. International Journal of General Systems 42 (2013) 386–394.
  • J. Ye, Vector similarity measures of simplified neutrosophic sets and their application in multicriteria decision making, Journal of Intelligent & Fuzzy Systems 16 (2014) 204–211.
  • P. D. Liu, Y. M. Wang, Multiple attribute decision making method based on single-valued neutrosophic normalized weighted Bonferroni mean, Neural Computing & Applications 25 (2014) 2001–2010.
  • P. D. Liu, Y. C. Chu, Y. W. Li, Y. B. Chen, Some generalized neutrosophic number Hamacher aggregation operators and their application to group decision making, Journal of Intelligent & Fuzzy Systems 16 (2014) 242–255.
  • A. W. Zhao, J. G. Du, H. J. Guan, Interval valued neutrosophic sets and multi-attribute decision-making based on generalized weighted aggregation operator, Journal of Intelligent & Fuzzy Systems 29 (2015) 2697–2706.
  • H. X. Sun, H. X. Yang, J. Z. Wu, O. Y. Yao, Interval neutrosophic numbers Choquet integral operator for multi-criteria decision making, Journal of Intelligent & Fuzzy Systems 28 (2015) 2443–2455.
  • J. J. Peng, J. Q. Wang, J. Wang, H. Y. Zhang, X. H. Chen, Simplified neutrosophic sets and their applications in multi-criteria group decision-making problems. International Journal of Systems Science 47 (2016) 2342–2358.
  • P. D. Liu, Y. M. Wang, Interval neutrosophic prioritized OWA operator and its application to multiple attribute decision making. Journal of Systems Science and Complexity 29 (2016) 681–697.
  • P. Biswas, S. Pramanik, B. C. Giri, TOPSIS method for multi-attribute group decision-making under single-valued neutrosophic environment. Neural Computing & Applications 27 (2016) 727–737.
  • J. Ye, Simplified neutrosophic harmonic averaging projection-based method for multiple attribute decision making problems. International Journal of Machine Learning and Cybernetics 8 (2017) 981–987.
  • A. Tu, J. Ye, B. Wang, Symmetry measures of simplified neutrosophic sets for multiple attribute decision-making problems. Symmetry 10 (2018) 144.
  • W. H. Cui, J. Ye, Improved symmetry measures of simplified neutrosophic sets and their decision-making method based on a sine entropy weight model. Symmetry 10(6) (2018) 225.
  • Y. X. Ma, J. Q. Wang, J. Wang, X. H. Wu, An interval neutrosophic linguistic multi-criteria group decision-making method and its application in selecting medical treatment options. Neural Computing & Applications 28(9) (2017) 2745–2765.
  • J. Ye, An extended TOPSIS method for multiple attribute group decision making based on single valued neutrosophic linguistic numbers. Journal of Intelligent & Fuzzy Systems 28(1) (2015) 247–255.
  • J. Ye, Some aggregation operators of interval neutrosophic linguistic numbers for multiple attribute decision making. Journal of Intelligent & Fuzzy Systems 27(5) (2014) 2231–2241.
  • J. Ye, Hesitant interval neutrosophic linguistic set and its application in multiple attribute decision making. International Journal of Machine Learning and Cybernetics 10(4) (2017) 667-678.
  • S. Broumi, F. Smarandache, Single valued neutrosophic trapezoid linguistic aggregation operators based multi-attribute decision making. Bull Pure Applied Sciences-Mathematics & Statistics 33(2) (2014) 135–155.
  • S. Broumi, J. Ye, F. Smarandache, An extended TOPSIS method for multiple attribute decision making based on interval neutrosophic uncertain linguistic variables. Neutrosophic Sets and Systems 8 (2015) 23–32.
  • Z. B. Fang, J. Ye, Multiple attribute group decision-making method based on linguistic neutrosophic numbers. Symmetry 9(7) (2017) 111.
  • C. X. Fan, J. Ye, K. L. Hu, E. Fan, Bonferroni mean operators of linguistic neutrosophic numbers and their multiple attribute group decision-making methods. Information 8(3) (2017) 107.
  • L. L. Shi, J. Ye, Cosine measures of linguistic neutrosophic numbers and their application in multiple attribute group decision-making. Information 8(4) (2017) 117.
  • L. L. Shi, J. Ye, Multiple attribute group decision-making method using correlation coefficients between linguistic neutrosophic numbers, Journal of Intelligent & Fuzzy Systems 35(1) (2018) 917-925.
  • P. D. Liu, X. L. You, Bidirectional projection measure of linguistic neutrosophic numbers and their application to multi-criteria group decision making, Computers & Industrial Engineering 128 (2019) 447-457.
  • F. Smarandache, n-Valued refined neutrosophic logic and its applications in physics, Progress in Physics 4 (2013) 143-146.
Year 2019, Issue: 28, 74 - 83, 07.05.2019

Abstract

Project Number

61703280

References

  • F. Smarandache, Neutrosophy: Neutrosophic probability, set, and logic. American Research Press, Rehoboth, USA, 1998.
  • Y. H. Guo, C. Zhou, H. P. Chan, A. Chughtai, J. Wei, L. M. Hadjiiski, E. A. Kazerooni, Automated iterative neutrosophic lung segmentation for image analysis in thoracic computed tomography. Medical Physics 40 (2013) 081912.
  • Y. H. Guo, A. Sengur, J. W. Tian, A novel breast ultrasound image segmentation algorithm based on neutrosophic similarity score and level set. Computer Methods and Programs in Biomedicine 123 (2016) 43–53.
  • K. M. Amin, A. I. Shahin, Y. H. Guo, A novel breast tumor classification algorithm using neutrosophic score features. Measurement 81 (2016) 210–220.
  • J. Ye, Improved cosine similarity measures of simplified neutrosophic sets for medical diagnoses. Artificial Intelligence in Medicine 63(3) (2015) 171–179.
  • J. Ye, J. Fu, Multi-period medical diagnosis method using a single valued neutrosophic similarity measure based on tangent function, Computer Methods and Programs in Biomedicine 123 (2016) 142-149.
  • J. Fu, J. Ye, Simplified neutrosophic exponential similarity measures for the initial evaluation/diagnosis of benign prostatic hyperplasia symptom, Symmetry 9(8) (2017) 154.
  • J. Ye, Fault diagnoses of steam turbine using the exponential similarity measure of neutrosophic numbers, Journal of Intelligent & Fuzzy Systems 30 (2016) 1927–1934.
  • J. Ye, Single valued neutrosophic similarity measures based on cotangent function and their application in the fault diagnosis of steam turbine, Soft Computing 21(3) (2017) 817-825.
  • J. Ye, Fault diagnoses of hydraulic turbine using the dimension root similarity measure of single-valued neutrosophic sets. Intelligent Automation & Soft Computing 24(1) (2018) 1-8.
  • J. Ye, Multicriteria decision-making method using the correlation coefficient under single-valued neutrosophic environment. International Journal of General Systems 42 (2013) 386–394.
  • J. Ye, Vector similarity measures of simplified neutrosophic sets and their application in multicriteria decision making, Journal of Intelligent & Fuzzy Systems 16 (2014) 204–211.
  • P. D. Liu, Y. M. Wang, Multiple attribute decision making method based on single-valued neutrosophic normalized weighted Bonferroni mean, Neural Computing & Applications 25 (2014) 2001–2010.
  • P. D. Liu, Y. C. Chu, Y. W. Li, Y. B. Chen, Some generalized neutrosophic number Hamacher aggregation operators and their application to group decision making, Journal of Intelligent & Fuzzy Systems 16 (2014) 242–255.
  • A. W. Zhao, J. G. Du, H. J. Guan, Interval valued neutrosophic sets and multi-attribute decision-making based on generalized weighted aggregation operator, Journal of Intelligent & Fuzzy Systems 29 (2015) 2697–2706.
  • H. X. Sun, H. X. Yang, J. Z. Wu, O. Y. Yao, Interval neutrosophic numbers Choquet integral operator for multi-criteria decision making, Journal of Intelligent & Fuzzy Systems 28 (2015) 2443–2455.
  • J. J. Peng, J. Q. Wang, J. Wang, H. Y. Zhang, X. H. Chen, Simplified neutrosophic sets and their applications in multi-criteria group decision-making problems. International Journal of Systems Science 47 (2016) 2342–2358.
  • P. D. Liu, Y. M. Wang, Interval neutrosophic prioritized OWA operator and its application to multiple attribute decision making. Journal of Systems Science and Complexity 29 (2016) 681–697.
  • P. Biswas, S. Pramanik, B. C. Giri, TOPSIS method for multi-attribute group decision-making under single-valued neutrosophic environment. Neural Computing & Applications 27 (2016) 727–737.
  • J. Ye, Simplified neutrosophic harmonic averaging projection-based method for multiple attribute decision making problems. International Journal of Machine Learning and Cybernetics 8 (2017) 981–987.
  • A. Tu, J. Ye, B. Wang, Symmetry measures of simplified neutrosophic sets for multiple attribute decision-making problems. Symmetry 10 (2018) 144.
  • W. H. Cui, J. Ye, Improved symmetry measures of simplified neutrosophic sets and their decision-making method based on a sine entropy weight model. Symmetry 10(6) (2018) 225.
  • Y. X. Ma, J. Q. Wang, J. Wang, X. H. Wu, An interval neutrosophic linguistic multi-criteria group decision-making method and its application in selecting medical treatment options. Neural Computing & Applications 28(9) (2017) 2745–2765.
  • J. Ye, An extended TOPSIS method for multiple attribute group decision making based on single valued neutrosophic linguistic numbers. Journal of Intelligent & Fuzzy Systems 28(1) (2015) 247–255.
  • J. Ye, Some aggregation operators of interval neutrosophic linguistic numbers for multiple attribute decision making. Journal of Intelligent & Fuzzy Systems 27(5) (2014) 2231–2241.
  • J. Ye, Hesitant interval neutrosophic linguistic set and its application in multiple attribute decision making. International Journal of Machine Learning and Cybernetics 10(4) (2017) 667-678.
  • S. Broumi, F. Smarandache, Single valued neutrosophic trapezoid linguistic aggregation operators based multi-attribute decision making. Bull Pure Applied Sciences-Mathematics & Statistics 33(2) (2014) 135–155.
  • S. Broumi, J. Ye, F. Smarandache, An extended TOPSIS method for multiple attribute decision making based on interval neutrosophic uncertain linguistic variables. Neutrosophic Sets and Systems 8 (2015) 23–32.
  • Z. B. Fang, J. Ye, Multiple attribute group decision-making method based on linguistic neutrosophic numbers. Symmetry 9(7) (2017) 111.
  • C. X. Fan, J. Ye, K. L. Hu, E. Fan, Bonferroni mean operators of linguistic neutrosophic numbers and their multiple attribute group decision-making methods. Information 8(3) (2017) 107.
  • L. L. Shi, J. Ye, Cosine measures of linguistic neutrosophic numbers and their application in multiple attribute group decision-making. Information 8(4) (2017) 117.
  • L. L. Shi, J. Ye, Multiple attribute group decision-making method using correlation coefficients between linguistic neutrosophic numbers, Journal of Intelligent & Fuzzy Systems 35(1) (2018) 917-925.
  • P. D. Liu, X. L. You, Bidirectional projection measure of linguistic neutrosophic numbers and their application to multi-criteria group decision making, Computers & Industrial Engineering 128 (2019) 447-457.
  • F. Smarandache, n-Valued refined neutrosophic logic and its applications in physics, Progress in Physics 4 (2013) 143-146.
There are 34 citations in total.

Details

Primary Language English
Subjects Applied Mathematics
Journal Section Research Article
Authors

Yanfei Zhu This is me

Wenhua Cui This is me

Jun Ye This is me

Project Number 61703280
Publication Date May 7, 2019
Submission Date June 18, 2019
Published in Issue Year 2019 Issue: 28

Cite

APA Zhu, Y., Cui, W., & Ye, J. (2019). New Correlation Coefficients between Linguistic Neutrosophic Numbers and Their Group Decision Making Method. Journal of New Theory(28), 74-83.
AMA Zhu Y, Cui W, Ye J. New Correlation Coefficients between Linguistic Neutrosophic Numbers and Their Group Decision Making Method. JNT. May 2019;(28):74-83.
Chicago Zhu, Yanfei, Wenhua Cui, and Jun Ye. “New Correlation Coefficients Between Linguistic Neutrosophic Numbers and Their Group Decision Making Method”. Journal of New Theory, no. 28 (May 2019): 74-83.
EndNote Zhu Y, Cui W, Ye J (May 1, 2019) New Correlation Coefficients between Linguistic Neutrosophic Numbers and Their Group Decision Making Method. Journal of New Theory 28 74–83.
IEEE Y. Zhu, W. Cui, and J. Ye, “New Correlation Coefficients between Linguistic Neutrosophic Numbers and Their Group Decision Making Method”, JNT, no. 28, pp. 74–83, May 2019.
ISNAD Zhu, Yanfei et al. “New Correlation Coefficients Between Linguistic Neutrosophic Numbers and Their Group Decision Making Method”. Journal of New Theory 28 (May 2019), 74-83.
JAMA Zhu Y, Cui W, Ye J. New Correlation Coefficients between Linguistic Neutrosophic Numbers and Their Group Decision Making Method. JNT. 2019;:74–83.
MLA Zhu, Yanfei et al. “New Correlation Coefficients Between Linguistic Neutrosophic Numbers and Their Group Decision Making Method”. Journal of New Theory, no. 28, 2019, pp. 74-83.
Vancouver Zhu Y, Cui W, Ye J. New Correlation Coefficients between Linguistic Neutrosophic Numbers and Their Group Decision Making Method. JNT. 2019(28):74-83.


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