Skip to main content

On the Use of Subproduct in Fuzzy Relational Compositions Based on Grouping Features

  • Conference paper
  • First Online:
Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications (IPMU 2018)

Abstract

Fuzzy relational compositions have been extended and studied from distinct perspectives, and their use on the classification problem has been already demonstrated too. One of the recent approaches foreshadowed the positive influence of the so-called grouping features. When this improvement is being applied, the universe of features is partitioned into a number of groups of features and then the relevant composition is applied. The use of the concept was demonstrated on the real classification of Odonata (dragonflies). This paper shows that the Bandler-Kohout subproduct may appropriately serve as the chosen compositions in order to obtain an effective tool. The concepts of excluding features and generalized quantifiers will be employed in the constructed method as well. Some interesting properties will be introduced and a real example of the influence of the new concept will be provided.

This research was partially supported by the NPU II project LQ1602 “IT4Innovations excellence in science” provided by the MŠMT.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

Notes

  1. 1.

    By the type we mean, e.g., a color or a morphological type and the features are particular colors (green, black, blue etc.) or particular morphological types (Anisoptera, Zygoptera), see [21].

  2. 2.

    These systems are formally based mainly on compositions and fuzzy quantifiers.

  3. 3.

    Here, we abstract from the more complicated cases with more illnesses at the same time as usually there is always “the one” we seek for.

References

  1. Bělohlávek, R.: Fuzzy Relational Systems: Foundations and Principles. Kluwer Academic, Plenum Press, Dordrecht, New York (2002)

    Book  Google Scholar 

  2. Bandler, W., Kohout, L.J.: Semantics of implication operators and fuzzy relational products. Int. J. Man Mach. Stud. 12(1), 89–116 (1980)

    Article  MathSciNet  Google Scholar 

  3. Pedrycz, W.: Applications of fuzzy relational equations for methods of reasoning in presence of fuzzy data. Fuzzy Sets Syst. 16, 163–175 (1985)

    Article  MathSciNet  Google Scholar 

  4. Štěpnička, M., De Baets, B., Nosková, L.: Arithmetic fuzzy models. IEEE Trans. Fuzzy Syst. 18, 1058–1069 (2010)

    Article  Google Scholar 

  5. Sanchez, E.: Resolution of composite fuzzy relation equations. Inf. Control 30, 38–48 (1976)

    Article  MathSciNet  Google Scholar 

  6. Di Nola, A., Sessa, S., Pedrycz, W., Sanchez, E.: Fuzzy Relation Equations and Their Applications to Knowledge Engineering. Kluwer, Boston (1989)

    Book  Google Scholar 

  7. De Baets, B.: Analytical solution methods for fuzzy relational equations. In: Dubois, D., Prade, H. (eds.) The Handbook of Fuzzy Set Series, vol. 1, pp. 291–340. Academic Kluwer Publ, Boston (2000)

    Google Scholar 

  8. Pedrycz, W.: Fuzzy relational equations with generalized connectives and their applications. Fuzzy Sets Syst. 10, 185–201 (1983)

    Article  MathSciNet  Google Scholar 

  9. Cao, N., Štěpnička, M.: Fuzzy relation equations with fuzzy quantifiers. In: Kacprzyk, J., Szmidt, E., Zadrożny, S., Atanassov, K.T., Krawczak, M. (eds.) IWIFSGN/EUSFLAT -2017. AISC, vol. 641, pp. 354–367. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-66830-7_32

    Chapter  Google Scholar 

  10. Štěpnička, M., De Baets, B.: Interpolativity of at-least and at-most models of monotone single-input single-output fuzzy rule bases. Inf. Sci. 234, 16–28 (2013)

    Article  MathSciNet  Google Scholar 

  11. Štěpnička, M., Jayaram, B.: Interpolativity of at-least and at-most models of monotone fuzzy rule bases with multiple antecedent variables. Fuzzy Sets Syst. 297, 26–45 (2016)

    Article  MathSciNet  Google Scholar 

  12. Kohout, L., Kim, E.: The role of BK-products of relations in soft computing. Soft Comput. 6, 92–115 (2002)

    Article  Google Scholar 

  13. Dubois, D., Prade, H.: Semantics of quotient operators in fuzzy relational databases. Fuzzy Sets Syst. 78, 89–93 (1996)

    Article  MathSciNet  Google Scholar 

  14. Pivert, O., Bosc, P.: Fuzzy Preference Queries to Relational Databases. Imperial College Press, London (2012)

    Book  Google Scholar 

  15. Delgado, M., Sánchez, D., Vila, M.A.: Fuzzy cardinality based evaluation of quantified sentences. Int. J. Approx. Reason. 23, 23–66 (2000)

    Article  MathSciNet  Google Scholar 

  16. Belohlavek, R.: Sup-t-norm and inf-residuum are one type of relational product: unifying framework and consequences. Fuzzy Sets Syst. 197, 45–58 (2012)

    Article  MathSciNet  Google Scholar 

  17. Běhounek, L., Daňková, M.: Relational compositions in fuzzy class theory. Fuzzy Sets Syst. 160(8), 1005–1036 (2009)

    Article  MathSciNet  Google Scholar 

  18. De Baets, B., Kerre, E.: Fuzzy relational compositions. Fuzzy Sets Syst. 60, 109–120 (1993)

    Article  MathSciNet  Google Scholar 

  19. Daňková, M.: Fuzzy relations and fuzzy functions in partial fuzzy set theory. In: Kacprzyk, J., Szmidt, E., Zadrożny, S., Atanassov, K.T., Krawczak, M. (eds.) IWIFSGN/EUSFLAT -2017. AISC, vol. 641, pp. 563–573. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-66830-7_50

    Chapter  Google Scholar 

  20. Cao, N., Štěpnička, M.: How to incorporate excluding features in fuzzy relational compositions and what for. In: Carvalho, J.P., Lesot, M.-J., Kaymak, U., Vieira, S., Bouchon-Meunier, B., Yager, R.R. (eds.) IPMU 2016. CCIS, vol. 611, pp. 470–481. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-40581-0_38

    Chapter  Google Scholar 

  21. Cao, N., Štěpnička, M., Burda, M., Dolný, A.: Excluding features in fuzzy relational compositions. Expert Syst. Appl. 81, 1–11 (2017)

    Article  Google Scholar 

  22. Štěpnička, M., Holčapek, M.: Fuzzy relational compositions based on generalized quantifiers. In: Laurent, A., Strauss, O., Bouchon-Meunier, B., Yager, R.R. (eds.) IPMU 2014. CCIS, vol. 443, pp. 224–233. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-08855-6_23

    Chapter  MATH  Google Scholar 

  23. Cao, N., Štěpnička, M., Holčapek, M.: An extension of fuzzy relational compositions using generalized quantifiers. In: Proceedings of 16th World Congress of the International Fuzzy Systems Association (IFSA) and 9th Conference of the European Society for Fuzzy-Logic and Technology (EUSFLAT). Advances in Intelligent Systems Research, vol. 89, pp. 49–58. Atlantis press, Gijón (2015)

    Google Scholar 

  24. Cao, N., Štěpnička, M., Holčapek, M.: Extensions of fuzzy relational compositions based on generalized quantifer. Fuzzy Sets Syst. 339, 73–98 (2018)

    Article  Google Scholar 

  25. Cao, N., Štěpnička, M.: Incorporation of excluding features in fuzzy relational compositions based on generalized quantifiers. In: Kacprzyk, J., Szmidt, E., Zadrożny, S., Atanassov, K.T., Krawczak, M. (eds.) IWIFSGN/EUSFLAT -2017. AISC, vol. 641, pp. 368–379. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-66830-7_33

    Chapter  Google Scholar 

  26. Cao, N., Štěpnička, M.: Fuzzy relational compositions based on grouping features. In: The 9th International Conference on Knowledge and Systems Engineering (KSE 2017), Hue, Vietnam, pp. 94–99. IEEE (2017)

    Google Scholar 

  27. Dvořák, A., Holčapek, M.: L-fuzzy quantifiers of type \(\langle 1\rangle \) determined by fuzzy measures. Fuzzy Sets Syst. 160(23), 3425–3452 (2009)

    Article  MathSciNet  Google Scholar 

  28. Klement, E.P., Mesiar, R., Pap, E.: A universal integral as common frame for Choquet and Sugeno integral. IEEE Trans. Fuzzy Syst. 18, 178–187 (2010)

    Article  Google Scholar 

  29. Peeva, K., Kyosev, Y.: Fuzzy Relational Calculus: Theory, Applications and Software. World Scientific, Singapore (2005)

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Nhung Cao or Martin Štěpnička .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Cao, N., Štěpnička, M., Burda, M., Dolný, A. (2018). On the Use of Subproduct in Fuzzy Relational Compositions Based on Grouping Features. In: Medina, J., Ojeda-Aciego, M., Verdegay, J., Perfilieva, I., Bouchon-Meunier, B., Yager, R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications. IPMU 2018. Communications in Computer and Information Science, vol 855. Springer, Cham. https://doi.org/10.1007/978-3-319-91479-4_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-91479-4_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91478-7

  • Online ISBN: 978-3-319-91479-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics