Skip to main content

Hybrid Connection Between Fuzzy Rough Sets and Ordered Fuzzy Numbers

  • Conference paper
  • First Online:
Fuzzy Techniques: Theory and Applications (IFSA/NAFIPS 2019 2019)

Abstract

Ordered Fuzzy Numbers (OFN) provide the ability of modeling data which is united with its trend. This paper presents a proposition of connecting the OFN model with the concept of information granules built as fuzzy rough sets. The procedure for gathering data and converting them into OFN is a new way of looking at transforming time series of sensor readings into granules. The introduction of the method is supported by an illustrative example. The introduced procedure for calculating similarity between OFNs allows hybridization with fuzzy rough set approach and derivation of lower and upper approximations of concepts.

The hybridization concepts presented in this paper were developed as a part of the project “The hybridization of selected methods of computational intelligence for modeling non-precision data” within the program “MINIATURA 1” funded by the National Science Centre of Poland. Project No. 2017/01/X/ST6/01675.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Cornelis, C., De Cock, M., Radzikowska, A.M.: Fuzzy rough sets: from theory into practice. In: Handbook of Granular Computing (Chap. 24), pp. 533–552. Wiley, Hoboken (2008). https://doi.org/10.1002/9780470724163.ch24

  2. Cornelis, C., Jensen, R., Hurtado, G., Śleȩzak, D.: Attribute selection with fuzzy decision reducts. Inf. Sci. 180(2), 209–224 (2010). https://doi.org/10.1016/j.ins.2009.09.008

    Article  MathSciNet  MATH  Google Scholar 

  3. Czerniak, J.M., Dobrosielski, W.T., Filipowicz, I.: Comparing fuzzy numbers using defuzzificators on OFN shapes. In: Prokopowicz et al. [18], pp. 99–132. https://doi.org/10.1007/978-3-319-59614-3_6

    Chapter  Google Scholar 

  4. Dubois, D., Prade, H.: Operations on fuzzy numbers. Int. J. Syst. Sci. 9(6), 613–626 (1978). https://doi.org/10.1080/00207727808941724

    Article  MathSciNet  MATH  Google Scholar 

  5. Dubois, D., Prade, H.: Rough fuzzy sets and fuzzy rough sets. Int. J. Gen. Syst. 17(2–3), 191–209 (1990). https://doi.org/10.1080/03081079008935107

    Article  MATH  Google Scholar 

  6. Fu, T.C.: A review on time series data mining. Eng. Appl. Artif. Intell. 24(1), 164–181 (2011). https://doi.org/10.1016/j.engappai.2010.09.007

    Article  Google Scholar 

  7. Klir, G.J.: Fuzzy arithmetic with requisite constraints. Fuzzy Sets Syst. 91(2), 165–175 (1997). https://doi.org/10.1016/S0165-0114(97)00138-3

    Article  MathSciNet  MATH  Google Scholar 

  8. Kosiński, W., Prokopowicz, P., Kacprzak, D.: Fuzziness - representation of dynamic changes by ordered fuzzy numbers. In: Seising, R. (ed.) Views on Fuzzy Sets and Systems from Different Perspectives: Philosophy and Logic, Criticisms and Applications, pp. 485–508. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-540-93802-6_24

    Chapter  MATH  Google Scholar 

  9. Kosiński, W., Prokopowicz, P., Rosa, A.: Defuzzification functionals of ordered fuzzy numbers. IEEE Tran. Fuzzy Syst. 21(6), 1163–1169 (2013). https://doi.org/10.1109/TFUZZ.2013.2243456

    Article  Google Scholar 

  10. Kosiński, W., Prokopowicz, P., Ślȩzak, D.: Ordered fuzzy numbers. Bull. Pol. Acad. Sci. Math. 51(3), 327–338 (2003)

    MathSciNet  MATH  Google Scholar 

  11. Kosiński, W., Prokopowicz, P., Ślȩzak, D.: Calculus with fuzzy numbers. In: Bolc, L., et al. (eds.) Intelligent Media Technology for Communicative Intelligence. LNCS, vol. 3490, pp. 21–28. Springer, Heidelberg (2005). https://doi.org/10.1007/11558637_3

    Chapter  Google Scholar 

  12. Mares, M.: Weak arithmetics of fuzzy numbers. Fuzzy Sets Syst. 91(2), 143–153 (1997)

    Article  MathSciNet  Google Scholar 

  13. Pawlak, Z.: Rough Sets – Theoretical Aspects of Reasoning About Data. Springer, Heidelberg (1991). https://doi.org/10.1007/978-94-011-3534-4

    Book  MATH  Google Scholar 

  14. Pawlak, Z., Skowron, A.: Rough sets: some extensions. Inf. Sci. 177(1), 28–40 (2007). https://doi.org/10.1016/j.ins.2006.06.006

    Article  MathSciNet  MATH  Google Scholar 

  15. Prokopowicz, P.: Flexible and simple methods of calculations on fuzzy numbers with the ordered fuzzy numbers model. In: Rutkowski, L., et al. (eds.) Artificial Intelligence and Soft Computing. LNCS, vol. 7894, pp. 365–375. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38658-9_33

    Chapter  Google Scholar 

  16. Prokopowicz, P.: Processing direction with ordered fuzzy numbers. In: Prokopowicz et al. [18], pp. 81–98. https://doi.org/10.1007/978-3-319-59614-3_5

    Chapter  Google Scholar 

  17. Prokopowicz, P.: The use of ordered fuzzy numbers for modelling changes in dynamic processes. Inf. Sci. 470, 1–14 (2019). https://doi.org/10.1016/j.ins.2018.08.045

    Article  MathSciNet  Google Scholar 

  18. Prokopowicz, P., Czerniak, J., Mikołajewski, D., Apiecionek, Ł., Ślȩzak, D. (eds.): Theory and Applications of Ordered Fuzzy Numbers: A Tribute to Professor Witold Kosiński. Studies in Fuzziness and Soft Computing, vol. 356. Springer, Heidelberg (2017). https://doi.org/10.1007/978-3-319-59614-3

    Book  MATH  Google Scholar 

  19. Prokopowicz, P., Pedrycz, W.: The directed compatibility between ordered fuzzy numbers - a base tool for a direction sensitive fuzzy information processing. In: Rutkowski, L., et al. (eds.) Artificial Intelligence and Soft Computing. LNCS, vol. 9119, pp. 249–259. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-319-19324-3_23

    Chapter  Google Scholar 

  20. Prokopowicz, P., Ślȩzak, D.: Ordered fuzzy numbers: Definitions and operations. In: Prokopowicz et al. [18], pp. 57–79. https://doi.org/10.1007/978-3-319-59614-3_4

    Chapter  Google Scholar 

  21. Qian, Y., Wang, Q., Cheng, H., Liang, J., Dang, C.: Fuzzy-rough feature selection accelerator. Fuzzy Sets Syst. 258, 61–78 (2015). https://doi.org/10.1016/j.fss.2014.04.029

    Article  MathSciNet  MATH  Google Scholar 

  22. Radzikowska, A.M., Kerre, E.E.: A comparative study of fuzzy rough sets. Fuzzy Sets Syst. 126(2), 137–155 (2002). https://doi.org/10.1016/S0165-0114(01)00032-X

    Article  MathSciNet  MATH  Google Scholar 

  23. Sanchez, E.: Solution of fuzzy equations with extended operations. Fuzzy Sets Syst. 12(3), 237–248 (1984)

    Article  MathSciNet  Google Scholar 

  24. Ślȩzak, D., Grzegorowski, M., Janusz, A., Kozielski, M., Nguyen, S.H., Sikora, M., Stawicki, S., Wróbel, Ł.: A framework for learning and embedding multi-sensor forecasting models into a decision support system: a case study of methane concentration in coal mines. Inf. Sci. 451–452, 112–133 (2018). https://doi.org/10.1016/j.ins.2018.04.026

    Article  MathSciNet  Google Scholar 

  25. Szczuka, M., Skowron, A., Jankowski, A., Ślȩzak, D.: Granular computing: from granules to systems. In: Wiley Encyclopedia of Electrical and Electronics Engineering, pp. 1–8. Wiley, Hoboken (2016). https://doi.org/10.1002/047134608X.W8293

  26. Wagenknecht, M., Hampel, R., Schneider, V.: Computational aspects of fuzzy arithmetics based on archimedean t-norms. Fuzzy Sets Syst. 123(1), 49–62 (2001). https://doi.org/10.1016/S0165-0114(00)00096-8

    Article  MathSciNet  MATH  Google Scholar 

  27. Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)

    Article  Google Scholar 

  28. Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning I. Inf. Sci. 8(3), 199–249 (1975). https://doi.org/10.1016/0020-0255(75)90036-5

    Article  MathSciNet  MATH  Google Scholar 

  29. Zadeh, L.A. (ed.): Computing with Words: Principal Concepts and Ideas. Studies in Fuzziness and Soft Computing, vol. 277. Springer, Heidelberg (2012)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Piotr Prokopowicz .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Prokopowicz, P., Szczuka, M. (2019). Hybrid Connection Between Fuzzy Rough Sets and Ordered Fuzzy Numbers. In: Kearfott, R., Batyrshin, I., Reformat, M., Ceberio, M., Kreinovich, V. (eds) Fuzzy Techniques: Theory and Applications. IFSA/NAFIPS 2019 2019. Advances in Intelligent Systems and Computing, vol 1000. Springer, Cham. https://doi.org/10.1007/978-3-030-21920-8_45

Download citation

Publish with us

Policies and ethics