Skip to main content
Log in

On the structure of metric spaces related to pre-rough logic

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

Abstract

The consideration of approximate reasoning merging rough approximation and formal deduction in pre-rough logic leads to three different types of metric spaces, which we call pre-rough logic metric space, pre-rough upper logic metric space and pre-rough lower logic metric space, respectively. In this paper, we investigate the structure of these three metric spaces mainly from the perspective of topology. It is shown that the three metric spaces have no isolated points. In addition, the continuity of logic connectives w.r.t. rough upper pseudo-metric and rough lower pseudo-metric is examined and the robust analysis of rough logic is also studied. Lastly, the notion of rough divergency degree of any logic theory is proposed and its topological characterization is presented.

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. Banerjee M, Chakraborty MK (1996) Rough sets through algebraic logic. Fundam Inform 28(3):211–221

    MathSciNet  MATH  Google Scholar 

  2. Banerjee M (1997) Rough sets and 3-valued Lukasiewicz logic. Fundam Inform 31(3):213–220

    MathSciNet  MATH  Google Scholar 

  3. Bunder MW, Banerjee M, Chakraborty MK (2008) Some rough consequence logics and their interrelations. In: Transactions on rough sets VIII. Springer, Berlin, pp 1–20

  4. Blackburn P, De Rijke M, Venema Y (2001) Modal Logic. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  5. Cattaneo G, Ciucci D (2002) Heyting Wajsberg algebras an an abstract environment linking fuzzy and rough sets. Lect Notes Artif Intell 2475:77–84

    MATH  Google Scholar 

  6. Chen HM, Li TR, Ruan D et al (2013) 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 

  7. Duntsch I (1997) A logic for rough sets. Theor Comput Sci 179(1):427–436

    Article  MathSciNet  MATH  Google Scholar 

  8. Demri S, Orlowska E (2002) Orlowska, incomplete information: structure, inference, complexity. Springer, Berlin

    Book  MATH  Google Scholar 

  9. Fitting M (1996) First-order logic and automated theorem proving. Springer, Berlin

    Book  MATH  Google Scholar 

  10. Gottwald S (2001) A treatise on many-valued logics. Research Studies Press, Baldock

    MATH  Google Scholar 

  11. Hajek P (1998) Metamathematics of fuzzy logic. Springer, Berlin

    Book  MATH  Google Scholar 

  12. Kelley JL (1975) General topology. Springer, Berlin

    MATH  Google Scholar 

  13. Li JH, Mei CL, Kumar CA, Zhang X (2013) On rule acquisition in decision formal contexts. Int J Mach Learn Cybernet 4(6):721–731

    Article  Google Scholar 

  14. Liang JY, Wang F, Dang CY, Qian YH (2014) A group incremental approach to feature selection applying rough set technique. IEEE Trans Knowl Data Eng 26(2):294–308

    Article  Google Scholar 

  15. Nakamura A (1996) A rough logic based on incomplete information and its application. Int J Approx Reason 15(4):367–378

    Article  MathSciNet  MATH  Google Scholar 

  16. Pawlak Z (1982) Rough sets. Int J Comput Inf Sci 11:341–356

    Article  MATH  Google Scholar 

  17. Pawlak Z (1991) Rough sets theoretical aspects of reasoning about data. Kluwer Academic Publiser, London

    MATH  Google Scholar 

  18. Pawlak Z (1997) Rough set approach to knowledge-based decision suport. Euro J Operat Res 99(1):48–57

    Article  MATH  Google Scholar 

  19. Pawlak Z (1998) Rough set theory and its applications to data analysis. Cybernet Syst Int J 29(7):661–688

    Article  MATH  Google Scholar 

  20. Pawlak Z, Polkowski L, Skowron A (2000) Rough sets and rough logic: a KDD perspective. In: Rough set methods and applications. Physica-Verlag HD, pp 583–646

  21. Pagliani P, Chakraborty MK (2008) A geometry of approximation: rough set theory: logic. Springer, Algebra and Topology of Conceptual Patterns

    Book  MATH  Google Scholar 

  22. She YH, He XL, Wang GJ (2011) Rough truth degrees of formulas and approximate reasoning in rough logic. Fundam Inform 107(1):67–83

    MathSciNet  MATH  Google Scholar 

  23. She YH (2014) On the rough consistency measures of logic theories and approximate reasoning in rough logic. Int J Approx Reason 55(1):486–499

    Article  MathSciNet  MATH  Google Scholar 

  24. She YH, He XL (2014) Rough approximation operators on \(R_{0}\)-algebras (nilpotent minimum algebras) with an application in formal logic \(\cal l\). Inf Sci 277:71–89

    Article  MATH  Google Scholar 

  25. Saha A, Sen J, Chakraborty MK (2014) Algebraic structures in the vicinity of pre-rough algebra and their logics. Inf Sci 282:296–320

    Article  MathSciNet  MATH  Google Scholar 

  26. Wang GJ, Zhou HJ (2010) Introduction to mathematical logic and resolution principle. Alpha Science Interlatinal Limited, Oxford

    MATH  Google Scholar 

  27. Wang GJ (2008) Non-classical mathematical logic and approximate reasoning (Second edition), Science Press (in Chinese)

  28. Zhai YH, Li DY, Qu KS (2014) Decision implications: a logical point of view. Int J Mach Learn Cybernet 5(4):509–516

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yanhong She.

Additional information

Project supported by the National Nature Science Fund of China under Grant 61472471 and 61103133, and the Natural Science Program for Basic Research of Shaanxi Province, China (No. 2014JQ1032).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

She, Y., He, X. & Ma, L. On the structure of metric spaces related to pre-rough logic. Int. J. Mach. Learn. & Cyber. 8, 537–546 (2017). https://doi.org/10.1007/s13042-015-0344-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-015-0344-7

Keywords

Navigation