Submit your research to the International Journal "Notes on Intuitionistic Fuzzy Sets". Contact us at nifs.journal@gmail.com
Call for Papers for the 27th International Conference on Intuitionistic Fuzzy Sets is now open!
Conference: 5–6 July 2024, Burgas, Bulgaria • EXTENDED DEADLINE for submissions: 15 APRIL 2024.

Issue:Identification of a sufficient number of the best attributes in the intuitionistic fuzzy models

From Ifigenia, the wiki for intuitionistic fuzzy sets and generalized nets
Jump to navigation Jump to search
shortcut
http://ifigenia.org/wiki/issue:nifs/29/2/144-156
Title of paper: Identification of a sufficient number of the best attributes in the intuitionistic fuzzy models
Author(s):
Eulalia Szmidt
Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447 Warsaw, Poland
Warsaw School of Information Technology, ul. Newelska 6, 01-447 Warsaw, Poland
szmidt@ibspan.waw.pl
Janusz Kacprzyk
Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447 Warsaw, Poland
Warsaw School of Information Technology, ul. Newelska 6, 01-447 Warsaw, Poland
kacprzyk@ibspan.waw.pl
Paweł Bujnowski
Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447 Warsaw, Poland
pbujno@ibspan.waw.pl
Presented at: 26th International Conference on Intuitionistic Fuzzy Sets, Sofia, 26—27 June 2023
Published in: Notes on Intuitionistic Fuzzy Sets, Volume 29 (2023), Number 2, pages 144–156
DOI: https://doi.org/10.7546/nifs.2023.29.2.144-156
Download:  PDF (342  Kb, Info)
Abstract: Dimension reduction of the models, i.e., pointing out only the necessary number of input variables (attributes, features) is an important task enabling the efficient performance of different algorithms. This paper is a continuation of our previous works on a new method of selection of the attributes in the models making use of Atanassov's intuitionistic fuzzy sets. We consider classification problems trying to point out the reduced number of the attributes and still obtain satisfactory results. We investigate the previously proposed method in more details comparing its performance with a well-known method of extraction parameters, namely Principal Component Analysis (PCA), and with a well-known method of selecting the attributes in which the so-called Gain Ratio is used. We illustrate our considerations using benchmark data from UCI Machine Learning Repository.
Keywords: Selection of attributes, Classification, Intuitionistic fuzzy sets, Principal Component Analysis, Gain Ratio.
AMS Classification: 03E72.
References:
  1. Antal, B., & Hajdu, A. (2014). An ensemble-based system for automatic screening of diabetic retinopathy. Knowledge-Based Systems, 60 20–27.
  2. Atanassov, K. T. (1983). Intuitionistic fuzzy sets. VII ITKR Session, Sofia, 20-23 June 1983 (Deposed in Centr. Sci.-Techn. Library of the Bulg. Acad. of Sci., 1697/84) (in Bulgarian). Reprinted: Int. J. Bioautomation, 2016, 20(S1), S1–S6.
  3. Atanassov, K. T. (1999). Intuitionistic Fuzzy Sets: Theory and Applications. Heidelberg: Springer-Physica Verlag.
  4. Atanassov, K. T. (2012). On Intuitionistic Fuzzy Sets Theory, Berlin: Springer.
  5. Atanassova, V. (2004). Strategies for decision making in the conditions of intuitionistic fuzziness. International Conference “8th Fuzzy Days”, Dortmund, Germany, 263–269.
  6. Bujnowski, P., Szmidt, E., & Kacprzyk, J. (2014). Intuitionistic fuzzy decision trees – A new approach. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., & Zurada, J. (Eds.). Artificial Intelligence and Soft Computing, Part I. Springer, Switzerland, 181–192.
  7. Bustince, H., Mohedano, V., Barrenechea, E., & Pagola, M. (2006). An algorithm for calculating the threshold of an image representing uncertainty through A-IFSs. Proceedings of IPMU’2006, 2383–2390.
  8. Hand, D. J., & Till, R. J. (2001). A simple generalization of the area under the ROC curve for multiple class classification problems. Machine Learning, 45, 171–186.
  9. Jackson, J. E. (1991). A User’s Guide to Principal Components New York: John Wiley and Sons.
  10. Jolliffe, I. T. (1986). Principal Component Analysis. Springer-Verlag.
  11. Maggiora, G., & Szmidt, E. (2021). An intuitionistic fuzzy set analysis of drug-target interactions. MATCH Communications in Mathematical and in Computer Chemistry, 85(3), 465–498.
  12. Mardia, K. V., Kent, J. T., & Bibby, J. M. (1995). Multivariate Analysis. Probability and Mathematical Statistics. Academic Press.
  13. Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1, 81–106.
  14. Roeva, O., & Michalikova, A. (2013). Generalized net model of intuitionistic fuzzy logic control of genetic algorithm parameters. Notes on Intuitionistic Fuzzy Sets, 19(2), 71–76.
  15. Szmidt, E. (2014). Distances and Similarities in Intuitionistic Fuzzy Sets. “Studies in Fuzziness and Soft Computing”, Vol. 307. Springer, Cham.
  16. Szmidt, E., & Baldwin, J. (2006). Intuitionistic fuzzy set functions, mass assignment theory, possibility theory and histograms. Proceedings of 2006 IEEE World Congress on Computational Intelligence, 237–243.
  17. Szmidt, E., & Kacprzyk, J. (1996). Group decision making via intuitionistic fuzzy sets. Proceedings of FUBEST’96, Sofia, Bulgaria, 107–112.
  18. Szmidt, E., & Kacprzyk, J. (1997). On measuring distances between intuitionistic fuzzy sets. Notes on Intuitionistic Fuzzy Sets, 3(4), 1–13.
  19. Szmidt, E., & Kacprzyk, J. (1998). Group decision making under intuitionistic fuzzy preference relations. Proceedings of IPMU’98, 172–178.
  20. Szmidt, E., & Kacprzyk, J. (1998). Applications of intuitionistic fuzzy sets in decision making. Proceedings of EUSFLAT’99, Navarra, Spain, 150–158.
  21. Szmidt, E., & Kacprzyk, J. (2000). Distances between intuitionistic fuzzy sets. Fuzzy Sets and Systems, 114(3), 505–518.
  22. Szmidt, E., & Kacprzyk, J. (2001). Entropy for intuitionistic fuzzy sets. Fuzzy Sets and Systems, 118 (3), 467–477.
  23. Szmidt, E., & Kacprzyk, J. (2001). Analysis of Consensus under intuitionistic fuzzy preferences. Proceedings of International Conference in Fuzzy Logic and Technology, Leicester, UK, 79–82.
  24. Szmidt, E., & Kacprzyk, J. (2002). Analysis of agreement in a group of experts via distances between intuitionistic fuzzy preferences. Proceedings of 9th International Conference IPMU 2002, Annecy, France, 1859–1865.
  25. Szmidt, E., & Kacprzyk, J. (2002). Evaluation of agreement in a group of experts via distances between intuitionistic fuzzy sets. Proceedings of IEEE Conference on Intelligent Systems, Varna, Bulgaria, 166–170.
  26. Szmidt, E., & Kacprzyk, J. (2004). A concept of similarity for intuitionistic fuzzy sets and its use in group decision making. Proceedings of IEEE Conference on Fuzzy Systems, Budapest, Hungary, 1129–1134.
  27. Szmidt, E., & Kacprzyk, J. (2005). New measures of entropy for intuitionistic fuzzy sets. Notes on Intuitionistic Fuzzy Sets, 11(2), 12–20.
  28. Szmidt, E., & Kacprzyk, J. (2005). Distances between intuitionistic fuzzy sets and their applications in reasoning. In: Halgamuge S. K., & Wang, L. (Eds.). Computational Intelligence for Modelling and Prediction. “Studies in Computational Intelligence”, Vol 2. Springer, 101–116.
  29. Szmidt, E., & Kacprzyk, J. (2005). A new concept of a similarity measure for intuitionistic fuzzy sets and its use in group decision making. In: Torra, V., Narukawa, Y., Miyamoto. S. (Eds.). Modelling Decisions for Artificial Intelligence. LNAI 3558, Springer, 272–282.
  30. Szmidt, E., & Kacprzyk, J. (2006). Distances between intuitionistic fuzzy sets: Straightforward approaches may not work. Proceedings of 3rd International IEEE Conference Intelligent Systems (IEEE IS’06), London, 716–721.
  31. Szmidt, E., & Kacprzyk, J. (2006). An application of intuitionistic fuzzy set similarity measures to a multi-criteria decision making problem. Proceedings of ICAISC 2006, LNAI 4029, Springer-Verlag, 314–323.
  32. Szmidt, E., & Kacprzyk, J. (2007). Some problems with entropy measures for the Atanassov intuitionistic fuzzy sets. Applications of Fuzzy Sets Theory, LNAI 4578, Springer-Verlag, 291–297.
  33. Szmidt, E., & Kacprzyk, J. (2007). A new similarity measure for Intuitionistic Fuzzy Sets: Straightforward approaches may not work. Proceedings of 2007 IEEE Conf. on Fuzzy Systems, 481–486.
  34. Szmidt, E., & Kacprzyk, J. (2009). Ranking of intuitionistic fuzzy alternatives in a multi-criteria decision making problem. Proceedings of NAFIPS 2009, Cincinnati, USA, June 14-17, 2009. DOI: 10.1109/NAFIPS.2009.5156417.
  35. Szmidt, E., & Kacprzyk, J. (2010). Dealing with typical values via Atanassov’s intuitionistic fuzzy sets. International Journal of General Systems, 39(5), 489–506.
  36. Szmidt, E., & Kacprzyk, J. (2011). Intuitionistic fuzzy sets – Two and three term representations in the context of a Hausdorff distance. Acta Universitatis Matthiae Belii, Series Mathematics, 19, 53–62.
  37. Szmidt, E., & Kacprzyk, J. (2012). A new approach to Principal Component Analysis for intuitionistic fuzzy data sets. In: Greco, S., et al. (Eds.). IPMU 2012, Part II, CCIS 298, Springer-Verlag Berlin Heidelberg, 529–538.
  38. Szmidt, E., & Kacprzyk, J. (2015). Two and three term representations of intuitionistic fuzzy sets: Some conceptual and analytic aspects. Proceedings of IEEE International Conference on Fuzzy Systems FUZZ-IEEE 2015, 1-8.
  39. Szmidt, E., & Kacprzyk, J. (2017). A perspective on differences between Atanassov’s intuitionistic fuzzy sets and interval-valued fuzzy sets. In: Torra, V., Dahlbom, A., & Narukawa, Y. (Eds.). Fuzzy Sets, Rough Sets, Multisets and Clustering “Studies in Computational Intelligence”, Vol. 671, 221–237, Springer.
  40. Szmidt, E., & Kacprzyk, J. (2022). Atanassov’s intuitionistic fuzzy sets demystified. In: Ciucci, D., Couso, I., Medina, J., Slezak, D., Petturiti, D., Bouchon-Meunier, B., & Yager, R. R. (Eds.). Information Processing and Management of Uncertainty in Knowledge-Based Systems – 19th International Conference (IPMU 2022), Milan, Italy, Part I, 517–527. “Communications in Computer and Information Science”, Vol. 1601, Springer.
  41. Szmidt, E., Kacprzyk, J., & Bujnowski, P. (2020). Attribute selection for sets of data expressed by intuitionistic fuzzy sets. Proceedings of 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Glasgow, UK, 1–7.
  42. Szmidt, E., Kacprzyk, J., & Bujnowski, P. (2021). Three term attribute description of Atanassov’s intuitionistic fuzzy sets as a basis of attribute selection. Proceedings of 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1–6.
  43. Szmidt, E., Kacprzyk, J., & Bujnowski, P. (2022). Ranking of alternatives described by Atanassov’s intuitionistic fuzzy sets – A critical review. Proceedings of 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1–7.
  44. Szmidt, E., Kacprzyk, J., & Bujnowski, P. (2022). Similarity measures for Atanassov’s intuitionistic fuzzy sets: Some dilemmas and challenges. Control and Cybernetics, 51(2), 249–266.
  45. Szmidt, E., & Kukier, M. (2006). Classification of imbalanced and overlapping classes using intuitionistic fuzzy sets. Proceedings of 3rd Int. IEEE Conference on Intelligent Systems (IEEE IS’06), London, 722–727.
  46. Szmidt, E., & Kukier, M. (2008). A new approach to classification of imbalanced classes via Atanassov’s intuitionistic fuzzy sets. Wang, H.-F. (Ed.). Intelligent Data Analysis: Developing New Methodologies Through Pattern Discovery and Recovery. Idea Group, 85–101.
  47. Szmidt, E., & Kukier, M. (2008). 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. “Studies in Computational Intelligence”. Springer, Berlin Heidelberg, 455–471.
  48. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353.
Citations:

The list of publications, citing this article may be empty or incomplete. If you can provide relevant data, please, write on the talk page.

Retrieved from "https://ifigenia.org/index.php?title=Issue:Identification_of_a_sufficient_number_of_the_best_attributes_in_the_intuitionistic_fuzzy_models&oldid=11585"