Skip to main content

A New Approach to Hellwig’s Method of Data Reduction for Atanassov’s Intuitionistic Fuzzy Sets

  • Conference paper
  • First Online:
  • 1270 Accesses

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 855))

Abstract

We propose a new approach to Hellwig’s method for the reduction of dimensionality of a data set using Atanassov’s intuitionistic fuzzy sets (A-IFSs). We are mainly concerned with the dimension reduction for sets of data represented as the A-IFSs, and provide an illustrative example results which are compared with the results obtained by using the PCA (Principal Component Analysis) method. Remarks on comparisons with some other methods are also mentioned.

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

Buying options

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

Learn about institutional subscriptions

References

  1. Atanassov, K.: Intuitionistic Fuzzy Sets. VII ITKR Session. Sofia (Centr. Sci.-Techn. Libr. of Bulg. Acad. of Sci., 1697/84) (1983) (in Bulgarian)

    Google Scholar 

  2. Atanassov, K.: Intuitionistic Fuzzy Sets: Theory and Applications. Springer, Heidelberg (1999). https://doi.org/10.1007/978-3-7908-1870-3

    Book  MATH  Google Scholar 

  3. Atanassov, K.T.: On Intuitionistic Fuzzy Sets Theory. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29127-2

    Book  MATH  Google Scholar 

  4. Atanassova, V.: Strategies for Decision Making in the Conditions of Intuitionistic Fuzziness. In: International Conference on 8th Fuzzy Days, Dortmund, Germany, pp. 263–269 (2004)

    Google Scholar 

  5. Bujnowski, P., Szmidt, E., Kacprzyk, J.: Intuitionistic fuzzy decision trees - a new approach. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014. LNCS (LNAI), vol. 8467, pp. 181–192. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07173-2_17

    Chapter  Google Scholar 

  6. Bustince, H., Mohedano, V., Barrenechea, E., Pagola, M.: An algorithm for calculating the threshold of an image representing uncertainty through A-IFSs. In: IPMU 2006, pp. 2383–2390 (2006)

    Google Scholar 

  7. Bustince, H., Mohedano, V., Barrenechea, E., Pagola, M.: Image thresholding using intuitionistic fuzzy sets. In: Atanassov, K., Kacprzyk, J., Krawczak, M., Szmidt, E. (eds.) Issues in the Representation and Processing of Uncertain and Imprecise Information. Fuzzy Sets, Intuitionistic Fuzzy Sets, Generalized Nets, and Related Topics. EXIT, Warsaw (2005)

    Google Scholar 

  8. Fang, Y.-C., Tzeng, Y.-F., Li, S.-X.: A Taguchi PCA fuzzy-based approach for the multi-objective extended optimization of a miniature optical engine. J. Phys. D Appl. Phys. 41(17), 175–188 (2008)

    Google Scholar 

  9. Hellwig, Z.: On the optimal choice of predictors. In: Gostkowski, Z. (ed.) Toward a System of Quantitative Indicators of Components of Human Resources Development, Study VI. UNESCO, Paris (1968)

    Google Scholar 

  10. Jackson, J.E.: A User’s Guide to Principal Components. Wiley, New York (1991)

    Book  Google Scholar 

  11. Jolliffe, I.T.: Principal Component Analysis. Springer, New York (1986). https://doi.org/10.1007/b98835

    Book  MATH  Google Scholar 

  12. Jolliffe, I.T.: Principal Component Analysis, 2nd edn. Springer, New York (2002). https://doi.org/10.1007/b98835

    Book  MATH  Google Scholar 

  13. Xia, L., Zhao, C.: The application of PCA-fuzzy probability analysis on risk evaluation of construction schedule of highway. In: IEEE 2010 International Conference on Logistics Systems and Intelligent Management, pp. 1230–1234 (2010)

    Google Scholar 

  14. Mardia, K.V., Kent, J.T., Bibby, J.M.: Multivariate Analysis. Probability and Mathematical Statistics. Academic Press, New York (1995)

    MATH  Google Scholar 

  15. Pearson, K.: On lines and planes of closest fit to systems of points in space. Phil. Mag. 6(2), 559–572 (1901)

    Google Scholar 

  16. Roeva, O., Michalikova, A.: Generalized net model of intuitionistic fuzzy logic control of genetic algorithm parameters. Notes on Intuitionistic Fuzzy Sets 19(2), 71–76 (2013). ISSN 1310–4926

    Google Scholar 

  17. Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1, 81–106 (1986)

    Google Scholar 

  18. Sebzalli, Y.M., Wang, X.Z.: Knowledge discovery from process operational data using PCA and fuzzy clustering. Eng. Appl. Artif. Intell. 14, 607–616 (2001)

    Article  Google Scholar 

  19. Szmidt, E.: Distances and Similarities in Intuitionistic Fuzzy Sets. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-01640-5

    Book  MATH  Google Scholar 

  20. Szmidt, E., Baldwin, J.: Intuitionistic fuzzy set functions, mass assignment theory, possibility theory and histograms. In: IEEE World Congress on Computational Intelligence 2006, pp. 237–243 (2006)

    Google Scholar 

  21. Szmidt, E., Kacprzyk, J.: Remarks on some applications of intuitionistic fuzzy sets in decision making. Notes IFS 2(3), 22–31 (1996c)

    MathSciNet  MATH  Google Scholar 

  22. Szmidt, E., Kacprzyk, J.: On measuring distances between intuitionistic fuzzy sets. Notes IFS 3(4), 1–13 (1997)

    MathSciNet  MATH  Google Scholar 

  23. Szmidt, E., Kacprzyk, J.: Group decision making under intuitionistic fuzzy preference relations. In: IPMU 1998, pp. 172–178 (1998)

    Google Scholar 

  24. Szmidt, E., Kacprzyk, J.: Distances between intuitionistic fuzzy sets. Fuzzy Sets Syst. 114(3), 505–518 (2000)

    Article  MathSciNet  Google Scholar 

  25. Szmidt, E., Kacprzyk, J.: Entropy for intuitionistic fuzzy sets. Fuzzy Sets Syst. 118(3), 467–477 (2001)

    Article  MathSciNet  Google Scholar 

  26. Szmidt, E., Kacprzyk, J.: Distances between intuitionistic fuzzy sets: straightforward approaches may not work. In: IEEE IS 2006, pp. 716–721 (2006)

    Google Scholar 

  27. Szmidt, E., Kacprzyk, J.: An application of intuitionistic fuzzy set similarity measures to a multi-criteria decision making problem. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 314–323. Springer, Heidelberg (2006). https://doi.org/10.1007/11785231_34

    Chapter  Google Scholar 

  28. Szmidt, E., Kacprzyk, J.: Some problems with entropy measures for the atanassov intuitionistic fuzzy sets. In: Masulli, F., Mitra, S., Pasi, G. (eds.) WILF 2007. LNCS (LNAI), vol. 4578, pp. 291–297. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-73400-0_36

    Chapter  MATH  Google Scholar 

  29. Szmidt, E., Kacprzyk, J.: A new similarity measure for intuitionistic fuzzy sets: straightforward approaches may not work. In: 2007 IEEE Conference on Fuzzy Systems, pp. 481–486 (2007a)

    Google Scholar 

  30. Szmidt, E., Kacprzyk, J.: A new approach to ranking alternatives expressed via intuitionistic fuzzy sets. In: Ruan, D., et al. (eds.) Computational Intelligence in Decision and Control, pp. 265–270. World Scientific (2008)

    Google Scholar 

  31. Szmidt, E., Kacprzyk, J.: Amount of information and its reliability in the ranking of Atanassov’s intuitionistic fuzzy alternatives. In: Rakus-Andersson, E., Yager, R., Ichalkaranje, N., Jain, L.C. (eds.) Recent Advances in Decision Making. SCI, vol. 222, pp. 7–19. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02187-9_2

    Google Scholar 

  32. Szmidt, E., Kacprzyk, J.: Ranking of intuitionistic fuzzy alternatives in a multi-criteria decision making problem. In: Proceedings of the conference: NAFIPS 2009, Cincinnati, USA, 14–17 June 2009. IEEE (2009). ISBN 978-1-4244-4577-6

    Google Scholar 

  33. Szmidt, E., Kacprzyk, J.: Dealing with typical values via Atanassov’s intuitionistic fuzzy sets. Int. J. General Syst. 39(5), 489–506 (2010)

    Article  MathSciNet  Google Scholar 

  34. Szmidt, E., Kacprzyk, J.: Correlation of intuitionistic fuzzy sets. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds.) IPMU 2010. LNCS (LNAI), vol. 6178, pp. 169–177. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14049-5_18

    Chapter  Google Scholar 

  35. Szmidt, E., Kacprzyk, J.: A new approach to principal component analysis for intuitionistic fuzzy data sets. In: Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., Yager, R.R. (eds.) IPMU 2012. CCIS, vol. 298, pp. 529–538. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31715-6_56

    Chapter  Google Scholar 

  36. Szmidt, E., Kukier, M.: Classification of imbalanced and overlapping classes using intuitionistic fuzzy sets. In: IEEE IS 2006, London, pp. 722–727 (2006)

    Google Scholar 

  37. Szmidt, E., Kukier, M.: A new approach to classification of imbalanced classes via atanassov’s intuitionistic fuzzy sets. In: Wang, H.-F. (ed.) Intelligent Data Analysis: Developing New Methodologies Through Pattern Discovery and Recovery, pp. 85–101. Idea Group (2008)

    Google Scholar 

  38. Szmidt, E., Kukier, M.: Atanassov’s intuitionistic fuzzy sets in classification of imbalanced and overlapping classes. In: Chountas, P., Petrounias, I., Kacprzyk, J. (eds.) Intelligent Techniques and Tools for Novel System Architectures. SCI, vol. 109, pp. 455–471. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-77623-9_26

    Chapter  Google Scholar 

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

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Eulalia Szmidt .

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

Szmidt, E., Kacprzyk, J. (2018). A New Approach to Hellwig’s Method of Data Reduction for Atanassov’s Intuitionistic Fuzzy Sets. 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_46

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-91479-4_46

  • 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