Skip to main content
Log in

Hierarchical algorithm for calculating approximation regions based on granular computing

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

Three approximation regions, namely positive region, negative region, and boundary region are fundamental concepts in rough set theory. How to calculate three approximation regions effectively is a crucial issue. Granular computing emphasizes solving a complex problem at multiple levels of granularity or abstraction, which can simplify the problem solving. Based on granular computing, we propose a hierarchical algorithm to calculate three approximation regions, which is fast and cost-sensitive. First, we construct three knowledge representation levels. Second, based on three knowledge representation levels, we calculate three approximation regions hierarchically. Considering the dynamic variation of objects is very common in real applications, we propose incremental hierarchical algorithms to calculate three approximation regions dynamically. At a high level of knowledge representation levels with coarse granularity, the proposed hierarchical algorithms can obtain inaccurate results with high efficiency and low cost. At a low level of knowledge representation levels with fine granularity, the proposed hierarchical algorithms can obtain accurate results with low efficiency and high cost. From high level to low level, we calculate three approximation regions hierarchically, reducing the computational complexity and cost. Experimental results demonstrate the effectiveness of the proposed algorithms.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Availability of data and materials

Data and materials will be made available on request.

Code availability

Code will be made available on request.

References

  1. Pawlak Z (1982) Rough sets. International journal of computer & information sciences 11(5):341–356

    Article  MathSciNet  Google Scholar 

  2. Yang X, Li T, Fujita H, Liu D, Yao Y (2017) A unified model of sequential three-way decisions and multilevel incremental processing. Knowl-Based Syst 134:172–188

    Article  Google Scholar 

  3. Sheeja T, Kuriakose AS (2018) A novel feature selection method using fuzzy rough sets. Comput Ind 97:111–116

    Article  Google Scholar 

  4. Liu K, Yang X, Yu H, Mi J, Wang P, Chen X (2019) Rough set based semi-supervised feature selection via ensemble selector. Knowl-Based Syst 165:282–296

    Article  Google Scholar 

  5. Chen H, Li T, Fan X, Luo C (2019) Feature selection for imbalanced data based on neighborhood rough sets. Inf Sci 483:1–20

    Article  ADS  Google Scholar 

  6. Peng X, Wen J, Li Z, Yang G, Zhou C, Reid A, Hepburn DM, Judd MD, Siew W (2017) Rough set theory applied to pattern recognition of partial discharge in noise affected cable data. IEEE Trans Dielectr Electr Insul 24(1):147–156

    Article  Google Scholar 

  7. Singh A, Tiwari V, Garg P, Tentu AN (2019) Reasoning for uncertainty and rough set-based approach for an efficient biometric identification: An application scenario. In: Verma NK, Ghosh AK (eds) Computational Intelligence: Theories, Applications and Future Directions -, vol II. Springer, Singapore, pp 465–476

    Google Scholar 

  8. Tan A, Wu W, Qian Y, Liang J, Chen J, Li J (2018) Intuitionistic fuzzy rough set-based granular structures and attribute subset selection. IEEE Trans Fuzzy Syst 27(3):527–539

    Article  Google Scholar 

  9. Vluymans S (2018) Dealing with Imbalanced and Weakly Labelled Data in Machine Learning Using Fuzzy and Rough Set Methods vol. 807. Springer, Ghent University. Faculty of Medicine and Health Sciences ; University of Granada. Department of Computer Science and Artificial Intelligence

  10. Chen H, Li T, Luo C, Horng S, Wang G (2015) A decision-theoretic rough set approach for dynamic data mining. IEEE Trans Fuzzy Syst 23(6):1958–1970

    Article  Google Scholar 

  11. Huang Y, Li T, Luo C, Fujita H, Horng S (2017) Matrix-based dynamic updating rough fuzzy approximations for data mining. Knowl-Based Syst 119:273–283

    Article  Google Scholar 

  12. Li Z, Shi K, Dey N, Ashour AS, Wang D, Balas VE, McCauley P, Shi F (2017) Rule-based back propagation neural networks for various precision rough set presented kansei knowledge prediction: a case study on shoe product form features extraction. Neural Comput Appl 28(3):613–630

    Article  Google Scholar 

  13. Zhang J, Li T, Ruan D, Liu D (2012) Neighborhood rough sets for dynamic data mining. Int J Intell Syst 27(4):317–342

    Article  Google Scholar 

  14. Hu J, Li T, Chen H, Zeng A (2015) An incremental learning approach for updating approximations in rough set model over dual universes. Int J Intell Syst 30(8):923–947

    Article  CAS  Google Scholar 

  15. Luo C, Wang S, Li T, Chen H, Lv J, Yi Z (2023) Spark rough hypercuboid approach for scalable feature selection. IEEE Trans Knowl Data Eng 35(3):3130–3144. https://doi.org/10.1109/TKDE.2021.3112520

    Article  Google Scholar 

  16. Luo C, Wang S, Li T, Chen H, Lv J, Yi Z (2022) Large-scale meta-heuristic feature selection based on bpso assisted rough hypercuboid approach. IEEE Transactions on Neural Networks and Learning Systems, 1–15. https://doi.org/10.1109/TNNLS.2022.3171614

  17. Luo C, Cao Q, Li T, Chen H, Wang S (2023) Mapreduce accelerated attribute reduction based on neighborhood entropy with apache spark. Expert Syst Appl 211:118554. https://doi.org/10.1016/j.eswa.2022.118554

    Article  Google Scholar 

  18. Luo C, Li T, Yi Z, Fujita H (2016) Matrix approach to decision-theoretic rough sets for evolving data. Knowl-Based Syst 99:123–134

    Article  Google Scholar 

  19. Chen H, Li T, Ruan D, Lin J, Hu C (2011) A rough-set-based incremental approach for updating approximations under dynamic maintenance environments. IEEE Trans Knowl Data Eng 25(2):274–284

    Article  Google Scholar 

  20. Hao C, Li J, Fan M, Liu W, Tsang EC (2017) Optimal scale selection in dynamic multi-scale decision tables based on sequential three-way decisions. Inf Sci 415:213–232

    Article  Google Scholar 

  21. Zhang Y, Li T, Luo C, Zhang J, Chen H (2016) Incremental updating of rough approximations in interval-valued information systems under attribute generalization. Inf Sci 373:461–475

    Article  Google Scholar 

  22. 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 

  23. Luo C, Li T, Chen H, Fujita H, Yi Z (2018) Incremental rough set approach for hierarchical multicriteria classification. Inf Sci 429:72–87

    Article  ADS  MathSciNet  Google Scholar 

  24. Shu W, Shen H (2014) Incremental feature selection based on rough set in dynamic incomplete data. Pattern Recogn 47(12):3890–3906

    Article  ADS  Google Scholar 

  25. Yu J, Xu W (2017) Incremental knowledge discovering in interval-valued decision information system with the dynamic data. Int J Mach Learn Cybern 8(3):849–864

    Article  Google Scholar 

  26. Luo C, Li T, Chen H, Fujita H, Yi Z (2016) Efficient updating of probabilistic approximations with incremental objects. Knowl-Based Syst 109:71–83

    Article  Google Scholar 

  27. Liu D, Li T, Zhang J (2014) A rough set-based incremental approach for learning knowledge in dynamic incomplete information systems. Int J Approximate Reasoning 55(8):1764–1786

    Article  MathSciNet  Google Scholar 

  28. Li S, Li T, Liu D (2013) Dynamic maintenance of approximations in dominance-based rough set approach under the variation of the object set. Int J Intell Syst 28(8):729–751

    Article  ADS  Google Scholar 

  29. Ciucci D (2010) Classification of dynamics in rough sets. In: International Conference on Rough Sets and Current Trends in Computing, pp. 257–266. Springer

  30. Liu D, Li T, Zhang J (2015) Incremental updating approximations in probabilistic rough sets under the variation of attributes. Knowl-Based Syst 73:81–96

    Article  Google Scholar 

  31. Li S, Li T (2015) Incremental update of approximations in dominance-based rough sets approach under the variation of attribute values. Inf Sci 294:348–361

    Article  MathSciNet  Google Scholar 

  32. Hu C, Liu S, Liu G (2017) Matrix-based approaches for dynamic updating approximations in multigranulation rough sets. Knowl-Based Syst 122:51–63

    Article  Google Scholar 

  33. Li T, Ruan D, Geert W, Song J, Xu Y (2007) A rough sets based characteristic relation approach for dynamic attribute generalization in data mining. Knowl-Based Syst 20(5):485–494

    Article  Google Scholar 

  34. Chen H, Li T, Ruan D (2012) Maintenance of approximations in incomplete ordered decision systems while attribute values coarsening or refining. Knowl-Based Syst 31:140–161

    Article  CAS  Google Scholar 

  35. Zeng A, Li T, Hu J, Chen H, Luo C (2017) Dynamical updating fuzzy rough approximations for hybrid data under the variation of attribute values. Inf Sci 378:363–388

    Article  MathSciNet  Google Scholar 

  36. Luo C, Li T, Chen H, Lu L (2015) Fast algorithms for computing rough approximations in set-valued decision systems while updating criteria values. Inf Sci 299:221–242

    Article  MathSciNet  Google Scholar 

  37. Pedrycz W (2013) Granular Computing: Analysis and Design of Intelligent Systems. CRC Press, Boca Raton

    Book  Google Scholar 

  38. Yao Y (2018) Three-way decision and granular computing. Int J Approximate Reasoning 103:107–123

    Article  Google Scholar 

  39. Yao Y (2015) The two sides of the theory of rough sets. Knowl-Based Syst 80:67–77

    Article  Google Scholar 

  40. UCI machine learning data repository (2020) Website. https://archive.ics.uci.edu/ml/index.php

  41. Ahmad A, Qamar U, Raza MS (2020) An optimized method to calculate approximations in dominance based rough set approach. Appl Soft Comput 97:106731

    Article  Google Scholar 

  42. Luo C, Li T, Yao Y (2017) Dynamic probabilistic rough sets with incomplete data. Inf Sci 417:39–54

    Article  Google Scholar 

  43. Guo Y, Tsang EC, Hu M, Lin X, Chen D, Xu W, Sang B (2020) Incremental updating approximations for double-quantitative decision-theoretic rough sets with the variation of objects. Knowl-Based Syst 189:105082

    Article  Google Scholar 

Download references

Funding

This work was supported by the National Natural Science Foundation of China (Nos. 62076002, 61402005, 61972001), the Natural Science Foundation of Anhui Province, China (Nos. 2008085MF194, 1308085QF114, 1908085MF188), the Higher Education Natural Science Foundation of Anhui Province, China (No. KJ2013A015).

Author information

Authors and Affiliations

Authors

Contributions

YX: Conceptualization, Methodology, Supervision, Writing—original draft. JZ: Software, Data curation, Writing—review & editing. WS: Software, Data curation, Writing—review & editing.

Corresponding author

Correspondence to Yi Xu.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Consent for publication

Manuscript is approved by all authors for publication.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, Y., Zhang, J. & Sun, W. Hierarchical algorithm for calculating approximation regions based on granular computing. Int. J. Mach. Learn. & Cyber. 15, 985–1005 (2024). https://doi.org/10.1007/s13042-023-01951-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-023-01951-1

Keywords

Navigation