Skip to main content
SpringerLink
Log in

Dynamic updating approximations of local generalized multigranulation neighborhood rough set

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

The approximation space in rough set theory is important for dealing with uncertainties. As the information contained in various information systems is constantly updated and changed with the development of information technology, how to effectively obtain the approximation space in dynamic environments is essential. The local rough set as an excellent model avoids unnecessary calculation of information granules, and can significantly improve learning efficiency. In this paper, we mainly investigate a dynamic approximation update mechanism of multigranulation data from local viewpoint. We first define a support and inclusion function to construct local generalized multigranulation neighborhood rough set model. Then, the dynamic updating process of global rough set and local rough set is analyzed when object chandes. Meanwhile, the corresponding dynamic update algorithms for dynamic objects are proposed based on local generalized multigranulation rough set model. The complexity analysis about them theoretically proves the efficiency of local dynamic algorithm compared with global algorithm and static algorithm. To illustrate the effectiveness of proposed algorithms, twelve datasets from UCI are adopted to contrast experiments.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Chen H, Li T, Cai Y et al (2016) Parallel attribute reduction in dominance-based neighborhood rough set. Inf Sci 373:351–368

    Article  Google Scholar 

  2. Dua D, Graff C (2019) UCI Machine Learning Repository http://archive.ics.uci.edu/ml. Irvine, CA: University of California, School of Information and Computer Science

  3. Fujita H, Gaeta A, Loia V et al (2020) Hypotheses analysis and assessment in counterterrorism activities: a method based on OWA and fuzzy probabilistic rough sets. IEEE Trans Fuzzy Syst 28(5):831–845

    Article  Google Scholar 

  4. Fujita H, Gaeta A, Loia V et al (2019) Resilience analysis of critical infrastructures: a cognitive approach based on granular computing. IEEE Trans. Cybern. 49(5):1835–1848

    Article  Google Scholar 

  5. Guo Y, Tsang ECC, Xu W, Chen D (2019) Local logical disjunction double-quantitative rough sets. Inf Sci 500:87–112

    Article  Google Scholar 

  6. Jiang Z, Liu K, Yang X et al (2020) Accelerator for supervised neighborhood based attribute reduction. Int J Approx Reason 119:122–150

    Article  MathSciNet  Google Scholar 

  7. Ju H, Ding W, Yang X et al (2021) Robust supervised rough granular description model with the principle of justifiable granularity. Appl Soft Comput 110:107612

    Article  Google Scholar 

  8. Kong Q, Zhang X, Xu W et al (2020) Attribute reducts of multi-granulation information system. Artif Intell Rev 53(2):1353–1371

    Article  Google Scholar 

  9. Li W, Xu W, Zhang X et al (2021) Updating approximations with dynamic objects based on local multigranulation rough sets in ordered information systems. Artif Intell Rev, https://doi.org/10.1007/s10462-021-10053-9

  10. Li W, Xue X, Xu W et al (2020) Double-quantitative variable consistency dominance-based rough set approach. Int J Approx Reason 124:1–16

    Article  MathSciNet  Google Scholar 

  11. Lin B, Zhang X (2019) Relative reduction of incomplete interval-valued decision information systems associated with evidence theory. J Inf Sci Eng 35(6):1377–1396

    Google Scholar 

  12. Liu K, Yang X, Fujita H et al (2019) An efficient selector for multi-granularity attribute reduction. Inf Sci 505:457–472

    Article  Google Scholar 

  13. Luo S, Miao D, Zhang Z et al (2020) A neighborhood rough set model with nominal metric embedding. Inf Sci 520:373–388

    Article  MathSciNet  Google Scholar 

  14. Pawlak Z (1998) Rough set. Int J Comput Inf Sci 11(5):341–356

    Article  Google Scholar 

  15. Qian Y, Liang X, Lin G et al (2017) Local multigranulation decision-theoretic rough sets. Int J Approx Reason 82:119–137

    Article  MathSciNet  Google Scholar 

  16. Qian Y, Liang X, Wang Q et al (2018) Local rough set: a solution to rough data analysis in big data. Int J Approx Reason 97:38–63

    Article  MathSciNet  Google Scholar 

  17. Qian Y, Liang J, Yao Y, et a. l. (2010) MGRS: A multi-granulation rough set. Inf Sci 180(6):949–970

    Article  MathSciNet  Google Scholar 

  18. Wang Q, Qian Y, Liang X et al (2018) Local neighborhood rough set. Knowl-Based Syst 153:53–64

    Article  Google Scholar 

  19. Xu W, Guo Y (2016) Generalized multigranulation double-quantitative decision-theoretic rough set. Knowl-Based Syst 105:190–205

    Article  Google Scholar 

  20. Xu W, Li W (2016) Granular computing approach to two-way learning based on formal concept analysis in fuzzy datasets. IEEE Trans Cybern 46(2):366–379

    Article  MathSciNet  Google Scholar 

  21. Xu W, Li W, Zhang X (2017) Generalized multigranulation rough sets and optimal granularity selection. GrC 2(4):271–288

    Google Scholar 

  22. Xu W, Sun W, Zhang X et al (2012) Multiple granulation rough set approach to ordered information systems. Int J Gen Syst 41(5):475–501

    Article  MathSciNet  Google Scholar 

  23. Xu W, Wang Q, Zhang X (2013) Multi-granulation rough sets based on tolerance relations. Soft Comput 17(7):1241–1252

    Article  Google Scholar 

  24. Xu W, Yu J (2017) A novel approach to information fusion in multi-source datasets: a granular computing viewpoint. Inf Sci 378:410–423

    Article  Google Scholar 

  25. Yang X, Li T, Liu D et al (2020) A multilevel neighborhood sequential decision approach of three-way granular computing. Inf Sci 538:119–141

    Article  MathSciNet  Google Scholar 

  26. Yang X, Li T, Liu D et al (2017) A unified framework of dynamic three-way probabilistic rough sets. Inf Sci 420:126–147

    Article  MathSciNet  Google Scholar 

  27. Yang W, Pawlak Z et al (1982) MGRS: A multi-granulation rough set. J Inform Technol 12(3):341–356

    Google Scholar 

  28. Yang L, Xu W, Zhang X et al (2020) Multi-granulation method for information fusion in multi-source decision information system. Int J Approx Reason 122:47–65

    Article  MathSciNet  Google Scholar 

  29. Yang L, Zhang X, Xu W et al (2019) Multi-granulation rough sets and uncertainty measurement for multi-source fuzzy information system. Int J Fuzzy Syst 21(6):1919–1937

    Article  MathSciNet  Google Scholar 

  30. Yu J, Chen M, Xu W (2017) Dynamic computing rough approximations approach to time-evolving information granule interval-valued ordered information system. Appl Soft Comput 60:18–29

    Article  Google Scholar 

  31. Yuan K, Li W, Xu W et al (2021) A comparative experimental evaluation on performance of type-1 and interval type-2 Takagi-Sugeno fuzzy models. Int J Mach Learn Cyb 12:2135–2150

    Article  Google Scholar 

  32. Zhang X, Gou H, Lv Z et al (2021) Double-quantitative distance measurement and classification learning based on the tri-level granular structure of neighborhood system. Knowl-Based Syst 217:106799

    Article  Google Scholar 

  33. Zhang C, Li D, Zhai Y et al (2019) Multigranulation rough set model in hesitant fuzzy information systems and its application in person-job fit. Int J Mach Learn Cyb 10(4):717–729

    Article  Google Scholar 

  34. Zhang X, Yao H, Lv Z et al (2021) Class-specific information measures and attribute reducts for hierarchy and systematicness. Inf Sci 563:196–225

    Article  MathSciNet  Google Scholar 

  35. Zhang J, Zhang X, Xu W (2018) Lower approximation reduction based on discernibility information tree in inconsistent ordered decision information systems. Symmetry 10(12):696

    Article  Google Scholar 

  36. Zhang J, Zhang X, Xu W et al (2019) Local multigranulation decision-theoretic rough set in ordered information systems. Soft Comput 23(24):13247–13261

    Article  Google Scholar 

  37. Zhou J, Lai Z, Miao D et al (2020) Multigranulation rough-fuzzy clustering based on shadowed sets. Inf Sci 507:553–573

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This paper is supported by the National Natural Science Foundation of China (Nos.61976245, 61472463, 61772002) and the Fundamental Research Funds for the Central Universities (No. XDJK2019B029).

Author information

Authors and Affiliations

  1. College of Artificial Intelligence, Southwest University, Chongqing, 400715, People’s Republic of China

    Weihua Xu & Wentao Li

  2. School of Mathematics Statistics, Southwest University, Chongqing, 400715, People’s Republic of China

    Kehua Yuan

Authors
  1. Weihua Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kehua Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wentao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Weihua Xu.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, W., Yuan, K. & Li, W. Dynamic updating approximations of local generalized multigranulation neighborhood rough set. Appl Intell 52, 9148–9173 (2022). https://doi.org/10.1007/s10489-021-02861-x

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-021-02861-x

Keywords

Search

Navigation