Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
139 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Foundational propositions of hesitant fuzzy soft $β$-covering approximation spaces (2403.05290v1)

Published 8 Mar 2024 in cs.LG and cs.LO

Abstract: Soft set theory serves as a mathematical framework for handling uncertain information, and hesitant fuzzy sets find extensive application in scenarios involving uncertainty and hesitation. Hesitant fuzzy sets exhibit diverse membership degrees, giving rise to various forms of inclusion relationships among them. This article introduces the notions of hesitant fuzzy soft $\beta$-coverings and hesitant fuzzy soft $\beta$-neighborhoods, which are formulated based on distinct forms of inclusion relationships among hesitancy fuzzy sets. Subsequently, several associated properties are investigated. Additionally, specific variations of hesitant fuzzy soft $\beta$-coverings are introduced by incorporating hesitant fuzzy rough sets, followed by an exploration of properties pertaining to hesitant fuzzy soft $\beta$-covering approximation spaces.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (35)
  1. Perceptual maps to aggregate assessments from different rating profiles: A hesitant fuzzy linguistic approach. Applied Soft Computing, 147:110803, 2023.
  2. Rough pythagorean fuzzy approximations with neighborhood systems and information granulation. Expert Systems with Applications, 218:119603, 2023.
  3. J. C. R. Alcantud and V. Torra. Decomposition theorems and extension principles for hesitant fuzzy sets. Information Fusion, 41:48–56, 2018.
  4. Hesitant fuzzy soft sets. Journal of New Results in Science, 3:98–107, 2013.
  5. Auto loan fraud detection using dominance-based rough set approach versus machine learning methods. Expert Systems with Applications, 163:113740, 2021.
  6. A decision-theoretic rough set approach for dynamic data mining. IEEE Transactions on fuzzy Systems, 23(6):1958–1970, 2015.
  7. Balanced scorecard-based analysis about european energy investment policies: A hybrid hesitant fuzzy decision-making approach with quality function deployment. Expert Systems With Applications, 115:152–171, 2019.
  8. J. L. García-Lapresta and D. Pérez-Román. Consensus-based clustering under hesitant qualitative assessments. Fuzzy Sets and Systems, 292:261–273, 2016.
  9. Recognition of cancer mediating biomarkers using rough approximations enabled intuitionistic fuzzy soft sets based similarity measure. Applied Soft Computing, 124:109052, 2022.
  10. Probabilistic dual hesitant fuzzy set and its application in risk evaluation. Knowledge-Based Systems, 127:16–28, 2017.
  11. Hierarchical structures and uncertainty measures for intuitionistic fuzzy approximation space. Information Sciences, 336:92–114, 2016.
  12. Intuitionistic fuzzy β𝛽\betaitalic_β-covering-based rough sets. Artificial Intelligence Review, 53(4):2841–2873, 2020.
  13. H. Jiang and B. Q. Hu. A decision-theoretic fuzzy rough set in hesitant fuzzy information systems and its application in multi-attribute decision-making. Information Sciences, 579:103–127, 2021.
  14. Analyzing uncertainty in cardiotocogram data for the prediction of fetal risks based on machine learning techniques using rough set. Journal of Ambient Intelligence and Humanized Computing, pages 1–13, 2021.
  15. Two-sided matching model for complex product manufacturing tasks based on dual hesitant fuzzy preference information. Knowledge-Based Systems, 186:104989, 2019.
  16. Air quality deterministic and probabilistic forecasting system based on hesitant fuzzy sets and nonlinear robust outlier correction. Knowledge-Based Systems, 237:107789, 2022.
  17. L. Li and Y. Xu. An extended hesitant fuzzy set for modeling multi-source uncertainty and its applications in multiple-attribute decision-making. Expert Systems With Applications, 238:121834, 2024.
  18. Foundational theories of hesitant fuzzy sets and hesitant fuzzy information systems and their applications for multi-strength intelligent classifiers. arXiv preprint arXiv:2311.04256, 2023.
  19. Three-way multi-attribute decision-making under the double hierarchy hesitant fuzzy linguistic information system. Applied Soft Computing, 154:111315, 2024.
  20. A. Mukherjee and A. Mukherjee. Interval-valued intuitionistic fuzzy soft rough approximation operators and their applications in decision making problem. Annals of Data Science, 9(3):611–625, 2022.
  21. M. Pant and N. Mehra. Strong (α,k)𝛼𝑘(\alpha,k)( italic_α , italic_k )-cut and computational-based segmentation based novel hesitant fuzzy time series forecasting model. Applied Soft Computing, 153:111251, 2024.
  22. Z. Pawlak. Rough sets. International Journal of Applied Mathematics and Computer Science, 11:341–356, 1982.
  23. M. Tishya and A. Anitha. Precipitation prediction by integrating rough set on fuzzy approximation space with deep learning techniques. Applied Soft Computing, 139:110253, 2023.
  24. V. Torra. Hesitant fuzzy sets. International Journal of Intelligent Systems, 25:529–539, 2010.
  25. Double-local rough sets for efficient data mining. Information Sciences, 571:475–498, 2021.
  26. J. Wang and X. Li. An overlap function-based three-way intelligent decision model under interval-valued fuzzy information systems. Expert Systems with Applications, 238:122036, 2024.
  27. M. Xia and Z. Xu. Hesitant fuzzy information aggregation in decision making. International Journal of Approximate Reasoning, 52:395–407, 2011.
  28. G. Xin and L. Ying. Multi-attribute decision-making based on comprehensive hesitant fuzzy entropy. Expert Systems With Applications, 237:121459, 2024.
  29. A novel multi-attribute decision-making method based on fuzzy rough sets. Computers & Industrial Engineering, 155:107136, 2021.
  30. G. Yu. Relationships between fuzzy approximation spaces and their uncertainty measures. Information Sciences, 528:181–204, 2020.
  31. K. Zhang and J. Dai. Redefined fuzzy rough set models in fuzzy β𝛽\betaitalic_β-covering group approximation spaces. Fuzzy Sets and Systems, 442:109–154, 2022.
  32. K. Zhang and J. Dai. Three-way multi-criteria group decision-making method in a fuzzy β𝛽\betaitalic_β-covering group approximation space. Information Sciences, 599:1–24, 2022.
  33. On multicriteria decision-making method based on a fuzzy rough set model with fuzzy α𝛼\alphaitalic_α-neighborhoods. IEEE Transactions on Fuzzy Systems, 29(9):2491–2505, 2020.
  34. X. Zhang and J. Wang. Fuzzy β𝛽\betaitalic_β-covering approximation spaces. International Journal of Approximate Reasoning, 126:27–47, 2020.
  35. Dynamic maintenance of updating rough approximations in interval-valued ordered decision systems. Applied Intelligence, pages 1–18, 2023.

Summary

We haven't generated a summary for this paper yet.

X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets