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Quantitative dominance-based neighborhood rough sets via fuzzy preference relations

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Abstract
Dominance relations exist extensively in decision-making problems. Dominance-based neighborhood rough sets (DNRS) using fuzzy preference relations (FPRs) are presented in this article to deal with attribute reduction in the large-scale decision-making problems. In this model, FPR is elicited to quantify the dominance-based rough set model, which can efficiently deal with the under-fitting problem of classical dominance-based rough sets. First, by formulating a quantified dominance-based neighborhood relation which satisfies reflexivity, the propositions of the quantified DNRSs are analyzed. Second, we propose approaches to attribute reduction based on upper-approximate and lower-approximate discernibility matrices, respectively. Furthermore, we evaluate that the novel model performs efficiently and effectively in time consumption and space storage by experimental analysis. Finally, combining with parallel computing, we demonstrate that the new model can be used to deal with attribute reduction of large-scale datasets effectively.
Keywords
Rough sets, Decision making, Information systems, Parallel processing, Numerical models, Computational modeling, Analytical models, Discernibility matrix, dominance relation, fuzzy preference relation (FPR), parallel computing, rough set

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MLA
Yang, Shuyun, et al. “Quantitative Dominance-Based Neighborhood Rough Sets via Fuzzy Preference Relations.” IEEE TRANSACTIONS ON FUZZY SYSTEMS, vol. 29, no. 3, 2021, pp. 515–29, doi:10.1109/TFUZZ.2019.2955883.
APA
Yang, S., Yang, H., De Baets, B., Jah, M., & Shi, G. (2021). Quantitative dominance-based neighborhood rough sets via fuzzy preference relations. IEEE TRANSACTIONS ON FUZZY SYSTEMS, 29(3), 515–529. https://doi.org/10.1109/TFUZZ.2019.2955883
Chicago author-date
Yang, Shuyun, Hongying Yang, Bernard De Baets, Moriba Jah, and Guang Shi. 2021. “Quantitative Dominance-Based Neighborhood Rough Sets via Fuzzy Preference Relations.” IEEE TRANSACTIONS ON FUZZY SYSTEMS 29 (3): 515–29. https://doi.org/10.1109/TFUZZ.2019.2955883.
Chicago author-date (all authors)
Yang, Shuyun, Hongying Yang, Bernard De Baets, Moriba Jah, and Guang Shi. 2021. “Quantitative Dominance-Based Neighborhood Rough Sets via Fuzzy Preference Relations.” IEEE TRANSACTIONS ON FUZZY SYSTEMS 29 (3): 515–529. doi:10.1109/TFUZZ.2019.2955883.
Vancouver
1.
Yang S, Yang H, De Baets B, Jah M, Shi G. Quantitative dominance-based neighborhood rough sets via fuzzy preference relations. IEEE TRANSACTIONS ON FUZZY SYSTEMS. 2021;29(3):515–29.
IEEE
[1]
S. Yang, H. Yang, B. De Baets, M. Jah, and G. Shi, “Quantitative dominance-based neighborhood rough sets via fuzzy preference relations,” IEEE TRANSACTIONS ON FUZZY SYSTEMS, vol. 29, no. 3, pp. 515–529, 2021.
@article{8697342,
  abstract     = {{Dominance relations exist extensively in decision-making problems. Dominance-based neighborhood rough sets (DNRS) using fuzzy preference relations (FPRs) are presented in this article to deal with attribute reduction in the large-scale decision-making problems. In this model, FPR is elicited to quantify the dominance-based rough set model, which can efficiently deal with the under-fitting problem of classical dominance-based rough sets. First, by formulating a quantified dominance-based neighborhood relation which satisfies reflexivity, the propositions of the quantified DNRSs are analyzed. Second, we propose approaches to attribute reduction based on upper-approximate and lower-approximate discernibility matrices, respectively. Furthermore, we evaluate that the novel model performs efficiently and effectively in time consumption and space storage by experimental analysis. Finally, combining with parallel computing, we demonstrate that the new model can be used to deal with attribute reduction of large-scale datasets effectively.}},
  author       = {{Yang, Shuyun and Yang, Hongying and De Baets, Bernard and Jah, Moriba and Shi, Guang}},
  issn         = {{1063-6706}},
  journal      = {{IEEE TRANSACTIONS ON FUZZY SYSTEMS}},
  keywords     = {{Rough sets,Decision making,Information systems,Parallel processing,Numerical models,Computational modeling,Analytical models,Discernibility matrix,dominance relation,fuzzy preference relation (FPR),parallel computing,rough set}},
  language     = {{eng}},
  number       = {{3}},
  pages        = {{515--529}},
  title        = {{Quantitative dominance-based neighborhood rough sets via fuzzy preference relations}},
  url          = {{http://doi.org/10.1109/TFUZZ.2019.2955883}},
  volume       = {{29}},
  year         = {{2021}},
}

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