Skip to main content
Log in

Multigranulation double-quantitative decision-theoretic rough sets based on logical operations

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

Abstract

As two important expanded quantification rough sets models, the multigranulation decision-theoretic rough sets mainly uses conditional probability to show relative quantitative information in multigranulation framework, and the graded multigranulation rough set is used to measure absolute quantitative information. However, they only consider the absolute quantitative (relative quantitative) information in granular structure, but do not consider the relative quantitative (absolute quantitative) information. It means that they cannot reflect a complete information. In order to overcome the defect, this paper proposes two pairs of multigranulation double-quantitative decision-theoretic rough sets models based on Bayesian decision and graded multigranulation rough sets, which essentially indicate the relative and absolute information quantification. After further studies to discuss decision rules and the inner relationship between these two models. Furthermore, we introduce an illustrative case to show the effectiveness and superiority of our proposed models, and the results show that our methods are effective for dealing with practical problems. Finally, we present some experiments based on UCI data sets showing the advantages of our proposed models in classification performance.

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.

Similar content being viewed by others

References

  1. Pawlak Z (1991) Rough sets: theoretical aspects of reasoning about data. Kluwer Academic, Dordrecht

    Book  MATH  Google Scholar 

  2. Pawlak Z, Skowron A (2007) Rough sets: some extensions. Inf Sci 117(1):28–40

    Article  MathSciNet  MATH  Google Scholar 

  3. Pawlak Z, Skowron A (2007) Rudiments of rough sets. Inf Sci 117(1):3–27

    Article  MathSciNet  MATH  Google Scholar 

  4. Qian YH, Liang JY, Pedrycz W (2010) Positive approximation: an accelerator for attribute reduction in rough set theory. Artif Intell 174(9):597–618

    Article  MathSciNet  MATH  Google Scholar 

  5. Yang XA, Wang GY, Yu H, Li TR (2014) Decision region distribution preservation reduction in decision-theoretic rough set model. Inf Sci 278:614–640

    Article  MathSciNet  MATH  Google Scholar 

  6. Yao YY, Zhao Y (2008) Attribute reduction in decision-theoretic rough set models. Inf Sci 178(17):3356–3373

    Article  MathSciNet  MATH  Google Scholar 

  7. Xu SP, Yang XB, Yu HL, Yu DJ et al (2016) Multi-label learning with label-specific feature reduction. Knowl-Based Syst 104:52–61

    Article  Google Scholar 

  8. Li JH, Ren Y, Mei CL, Qian YH et al (2016) A comparative study of multigranulation rough sets and concept lattices via rule acquisition. Knowl-Based Syst 91:152–164

    Article  Google Scholar 

  9. Chen Y, Liu KY, Song JJ, Fujita H et al (2020) Attribute group for attribute reduction. Inf Sci 535:64–80

    Article  MATH  Google Scholar 

  10. Liu KY, Yang XB, Fujita H, Liu D et al (2019) An efficient selector for multi-granularity attribute reduction. Inf Sci 505:457–472

    Article  Google Scholar 

  11. Jiang ZH, Liu KY, Yang XB, Yu HL et al (2020) Accelerator for supervised neighborhood based attribute reduction. Int J Approx Reason 119:122–150

    Article  MathSciNet  MATH  Google Scholar 

  12. Lin TY (2000) Data mining and machine oriented modeling: a granular computing approach. Appl Intell 13(2):113–124

    Article  Google Scholar 

  13. Liu D, Li T, Liang D (2014) Incorporating logistic regression to decision-theoretic rough sets for classifications. Int J Approx Reason 55(1):197–210

    Article  MathSciNet  MATH  Google Scholar 

  14. Ju HR, Ding WP, Yang XB, Fujita H et al (2021) Robust supervised rough granular description model with the principle of justifiable granularity. Appl Soft Comput 110:107612

    Article  Google Scholar 

  15. Skowron A (1995) Extracting laws from decision tables: a rough set approach. Comput Intell 11(2):371–388

    Article  MathSciNet  Google Scholar 

  16. Xu J, Miao D, Zhang Y, Zhang Z (2017) A three-way decisions model with probabilistic rough sets for stream computing. Int J Approx Reason 88:1–22

    Article  MathSciNet  MATH  Google Scholar 

  17. Xu WH, Li MM, Wang XZ (2017) Information Fusion Based on Information Entropy in Fuzzy Multi-source Incomplete Information System. Int J Fuzzy Syst 19(4):1200–1216

    Article  Google Scholar 

  18. Rebolledo M (2006) Rough intervals-enhancing intervals for qualitative modeling of technical systems. Artif Intell 170:667–685

    Article  Google Scholar 

  19. Shen Q, Chouchoulas A (2002) A rough-fuzzy approach for generating classification rules. Pattern Recogn 35(11):2425–2438

    Article  MATH  Google Scholar 

  20. Xu ZB, Liang JY, Dang CY, Chin KS (2002) Inclusion degree: a perspective on measures for rough set data analysis. Inform Sci 141:227–236

    Article  MathSciNet  MATH  Google Scholar 

  21. Zeng A, Pan D, Zheng QL, Peng H (2006) Knowledge acquisition based on rough set theory and principal component analysis. IEEE Intell Syst 21(2):78–85

    Article  Google Scholar 

  22. Yang X, Liu D, Yang XB, Liu KY et al (2021) Incremental fuzzy probability decision-theoretic approaches to dynamic three-way approximations. Inf Sci 550:71–90

    Article  MathSciNet  Google Scholar 

  23. Dou HL, Yang XB, Song XN, Yu HL et al (2016) Decision-theoretic rough set: a multicost strategy. Knowl-Based Syst 91:71–83

    Article  Google Scholar 

  24. Wang X, Wang PX, Yang XB, Yao YY (2021) Attribution reduction based on sequential three-way search of granularity. Int J Mach Learn Cybern 12:1439–1458

    Article  Google Scholar 

  25. Düntsch I, Gediga G (1998) Uncertainty measures of rough set prediction. Artif Intell 106(1):109–137

    Article  MathSciNet  MATH  Google Scholar 

  26. Liang JY, Dang CY, Chin KS, Richard Yam CM (2002) A new method for measuring uncertainty and fuzziness in rough set theory. Int J Gen Syst 31(4):331–342

    Article  MathSciNet  MATH  Google Scholar 

  27. Jensen R, Shen Q (2007) Fuzzy-rough sets assisted attribute selection. IEEE Trans Fuzzy Syst 15(1):73–89

    Article  Google Scholar 

  28. Jeon G, Kim D, Jeong J (2006) Rough sets attributes reduction based expert system in interlaced video sequences. IEEE Trans Consum Electron 52(4):1348–1355

    Article  Google Scholar 

  29. Xu WH, Wang QR, Zhang XT (2013) Multi-granulation rough sets based on tolerance relations. Soft Comput 17(7):1241–1252

    Article  MATH  Google Scholar 

  30. Greco S, Matarazzo B, Słowiński R (2002) Rough approximation by dominance relations. Int J Intell Syst 17(2):153–171

    Article  MATH  Google Scholar 

  31. Hu QH, Yu DR, Liu JF, Wu CX (2008) Neighborhood rough set based heterogeneous feature subset selection. Inf Sci 178(18):3577–3594

    Article  MathSciNet  MATH  Google Scholar 

  32. Nanda S, Majumdar S (1992) Fuzzy rough sets. Fuzzy Sets Syst 45(2):157–160

    Article  MathSciNet  MATH  Google Scholar 

  33. Yao YY (2008) Probabilistic rough set approximations. Int J Approx Reason 49(2):255–271

    Article  MATH  Google Scholar 

  34. Yao YY (2010) Three-way decisions with probabilistic rough sets. Inf Sci 180(3):341–353

    Article  MathSciNet  Google Scholar 

  35. Ziarko W (2008) Probabilistic approach to rough sets. Int J Approx Reason 49(2):272–284

    Article  MathSciNet  MATH  Google Scholar 

  36. Yao YY, Wong SKM, Lingras PJ (1990) A decision-theoretic rough set model. Int Conf Methodol Intell Syst 5:17–24

    MathSciNet  Google Scholar 

  37. Slezak D, Ziarko W (2005) The investigation of the Bayesian rough set model. Int J Approx Reason 40(1):81–91

    Article  MathSciNet  MATH  Google Scholar 

  38. Yao YY, Zhou B (2016) Two bayesian approaches to rough sets. Eur J Oper Res 251(3):904–917

    Article  MathSciNet  MATH  Google Scholar 

  39. Ziarko W (1993) Variable precision rough set model. J Comput Syst Sci 46(1):39–59

    Article  MathSciNet  MATH  Google Scholar 

  40. Herbert JP, Yao JT (2011) Game-theoretic rough sets. Fundam Inform 108(3–4):267–286

    Article  MathSciNet  MATH  Google Scholar 

  41. Greco S, Matarazzo B, Słowiński R (2008) Parameterized rough set model using rough membership and Bayesian confirmation measures. Int J Approx Reason 49(2):285–300

    Article  MathSciNet  MATH  Google Scholar 

  42. Fang BW, Hu BQ (2016) Probabilistic graded rough set and double relative quantitative decision-theoretic rough set. Int J Approx Reason 74:1–12

    Article  MathSciNet  MATH  Google Scholar 

  43. Zhang XY, Miao DQ (2013) Two basic double-quantitative rough set models of precision and grade and their investigation using granular computing. Int J Approx Reason 54(8):1130–1148

    Article  MathSciNet  MATH  Google Scholar 

  44. Yao YY, Lin TY (1996) Generalization of rough sets using modal logic. Intell Autom Soft Comput 2(2):103–119

    Article  Google Scholar 

  45. Li WT, Xu WH (2015) Double-quantitative decision-theoretic rough set. Inf Sci 316:54–67

    Article  MATH  Google Scholar 

  46. Fan BJ, Tsang Eric CC, Xu WH, Yu JH (2017) Double-quantitative rough fuzzy set based decisions: a logical operations method. Inf Sci 378:264–281

    Article  MATH  Google Scholar 

  47. Yu JH, Zhang B, Chen MH, Xu WH (2018) Double-quantitative decision-theoretic approach to multigranulation approximate space. Int J Approx Reason 98:236–258

    Article  MathSciNet  MATH  Google Scholar 

  48. Zhang XY, Mo ZW, Xiong F, Cheng W (2012) Comparative study of variable precision rough set model and graded rough set model. Int J Approx Reason 53(1):104–116

    Article  MathSciNet  MATH  Google Scholar 

  49. Qian YH, Liang JY (2006) Rough set method based on multi-granulations. In: Proceedings of 5th IEEE Conferenceon Granular Computing pp.297-304

  50. Qian YH, Zhang H, Sang YL, Liang JY (2014) Multigranulation decision-theoretic rough sets. Int J Approx Reason 55:225–237

    Article  MathSciNet  MATH  Google Scholar 

  51. Qian YH, Dang CY, Liang JY (2007) MGRS in incomplete information systems. In: Proceedings of 2007 IEEE Conference on Granular Computing pp.163-168

  52. Qian YH, Liang JY, Dang CY (2010) Incomplete multigranulation rough set. IEEE Trans Syst Man Cybern Part A 20:420–431

    Article  Google Scholar 

  53. Qian YH, Liang JY, Li DY, Wang F, Ma NN (2010) Approximation reduction in inconsistent incomplete decision tables. Knowl-Based Syst 23:427–433

    Article  Google Scholar 

  54. Sun BZ, Qi C, Ma WM, Wang T et al (2020) Variable precision diversified attribute multigranulation fuzzy rough set-based multi-attribute group decision making problems. Comput Ind Eng 142:106331

    Article  Google Scholar 

  55. Pang JF, Guan XQ, Liang JY, Wang BL et al (2020) Multi-attribute group decision-making method based on multi-granulation weights and three-way decisions. Int J Approx Reason 117:122–147

    Article  MathSciNet  MATH  Google Scholar 

  56. Yang L, Xu WH, Zhang XY, Sang BB (2020) Multi-granulation method for information fusion in multi-source decision information system. Int J Approx Reason 122:47–65

    Article  MathSciNet  MATH  Google Scholar 

  57. Li MM, Chen MH, Xu WH (2019) Double quantitative multigranulation decision theoretic rough fuzzy set model. Int J Mach Learn Cybern 10(11):3225–3244

    Article  Google Scholar 

  58. Wu MF (2010) Fuzzy rough set model based on multi-granulations. 2010 International Conference on Computer Engineering and Technology pp 271-275

  59. Zhang M, Tang ZM, Xu WY, Yang XB (2011) A variable muitlgranulation rough sets approach. In: Proceedings of the 7th international conference on Intelligent Computing: bioinspired computing and applications pp 315-322

  60. Liu CH, Wang MZ (2011) Covering fuzzy rough set based on multi-granulations. In: International Conference on Uncertainty Reasoning and Knowledge Engineering pp 146-149

  61. Yang XB, Song XN, Dou HL, Yang JY (2011) Multi-granulation rough set: from crisp to fuzzy case. Ann Fuzzy Math Inform 1(1):55–70

    MathSciNet  MATH  Google Scholar 

  62. Yang XB, Qian YH, Yang JY (2012) Hierarchical structures on multigranulation spaces. J Comput Sci Technol 27(6):1169–1183

    Article  MathSciNet  MATH  Google Scholar 

  63. She YH, He XL (2012) On the structure of the multigranulation rough set model. Knowl-Based Syst 36:81–92

    Article  Google Scholar 

  64. Qian YH, Liang XY, Lin GP, Guo Q et al (2017) Local multigranulation decision-theoretic rough sets. Int J Approx Reason 82:119–137

    Article  MathSciNet  MATH  Google Scholar 

  65. Wu ZY, Zhong PH, Hu JG (2014) Graded multi-granulation rough sets. Fuzzy Syst Math 28(3):165–172 (in Chinese)

    MathSciNet  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the anonymous referees for their valuable comments and helpful suggestions. This work is supported by the National Natural Science Foundation of China (No. 11771111), and Mengmeng Li is supported by the China Scholarship Council under Grant No. 202006120274.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Minghao Chen.

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

Li, M., Zhang, C., Chen, M. et al. Multigranulation double-quantitative decision-theoretic rough sets based on logical operations. Int. J. Mach. Learn. & Cyber. 13, 1661–1684 (2022). https://doi.org/10.1007/s13042-021-01476-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-021-01476-5

Keywords

Navigation