8-9 October 2020 • Burgas, Bulgaria

Submission: 15 May 2020 • Notification: 31 May 2020 • Final Version: 15 June 2020

Issue:A new approach for an intuitionistic fuzzy Sugeno integral for decision making

From Ifigenia, the wiki for intuitionistic fuzzy sets and generalized nets
Jump to: navigation, search
Title of paper: A new approach for an intuitionistic fuzzy Sugeno integral for decision making
Gabriela E. Martínez
Tijuana Institute of Technology, Tijuana, Mexico
Patricia Melin
Tijuana Institute of Technology, Tijuana, Mexico
pmelinAt sign.pngtectijuana.mx
Oscar Castillo
Tijuana Institute of Technology, Tijuana, Mexico
Published in: Notes on Intuitionistic Fuzzy Sets, Volume 25 (2019), Number 2, pages 41–52
DOI: https://doi.org/10.7546/nifs.2019.25.2.41-52
Download: Download-icon.png PDF (178  Kb, Info) Download-icon.png
Abstract: In this paper, an extension of the Sugeno integral using intuitionistic fuzzy sets is presented. The proposed method enables the calculation of the Sugeno integral for combining multiple source of information with a degree of membership and non-membership using intuitionistic fuzzy sets. The proposed method is used as aggregation operator to combine the modules output of a modular neural network for face recognition. In this paper, the focus is on aggregation operator that use measures with intuitionistic fuzzy sets, in particular the Sugeno integral. The performance of the proposed method is compared with other aggregation operators, such as the traditional Sugeno integral using the ORL database.
Keywords: Aggregation operators, Sugeno integral, Modular neural networks, Intuitionistic fuzzy sets
AMS Classification: 03E72
  1. Atanassov, K. (1983). Intuitionistic fuzzy sets, VII ITKR’s Session, Sofia, June 1983 (Deposed in Central Sci.- Techn. Library of Bulg. Acad. Of Sci., 1697/84, in Bulgarian). Reprinted: Int. J. Bioautomation, 2016, 20 (S1), S1–S6.
  2. Atanassov, K. (1986). Intuitionistic fuzzy sets, Fuzzy Sets and Systems, 20 (1), 87–96.
  3. Atanassov, K. (1994). New operations defined over the intuitionistic fuzzy sets, Fuzzy Sets and Systems, 61, 137–142.
  4. Atanassov, K. (1999). Intuitionistic Fuzzy Sets: Theory and Applications, Heidelberg: Physica-Verlag.
  5. Atanassov, K., Vassilev, P., & Tsvetkov, R. (2013). Intuitionistic Fuzzy Sets, Measures and Integrals, Bulgarian Academic Monographs Vol. 12, Sofia: “Prof. Marin Drinov” Academic Publishing House.
  6. Bezdek, J. C., Keller, J., & Pal, N. R. (2005). Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. New York: Springer-Verlag.
  7. Choquet, G. (1953). Theory of capacities. Ann. Inst. Fourier, Grenoble, 5, 131–295.
  8. Database ORL Face. (2012, November). Cambridge University Computer Laboratory. Retrieved from: http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html.
  9. González, C. I., Melin, P., Castro, J. R., Castillo, O. & Mendoza, O. (2016). Optimization of interval type-2 fuzzy systems for image edge detection. Appl. Soft Comput. 47: 631–643.
  10. Klir, G. (2005). Uncertainty and Information. Hoboken, NJ: Wiley.
  11. Lei, Y., Liu, J., & Yin, H. (2016). Intrusion Detection Techniques Based on Improved Intuitionistic Fuzzy Neural Networks, 2016 International Conference on Intelligent Networking and Collaborative Systems (INCoS), Ostrawva, 518–521.
  12. Liu, Y, & Kong, Z. (2012). Interval intuitionistic fuzzy-valued Sugeno integral, Proc. of 9th International Conference on Fuzzy Systems and Knowledge Discovery, Sichuan, 89–92.
  13. Martínez, G. E., Mendoza O., Castro, J. R., Melin, P., & Castillo, O. (2014). Choquet Integral with Interval Type 2 Sugeno Measures as an Integration Method for Modular Neural Networks. Proc. of WCSC 2014, 71–86.
  14. Melin, P., & Castillo, O. (2001). Intelligent control of complex electrochemical systems with a neuro-fuzzy-genetic approach, IEEE Transactions on Industrial Electronics, 48 (5), 951–955.
  15. Melin, P., Martinez, G. E., & Tsvetkov, R. (2017). Choquet and Sugeno integrals and intuitionistic fuzzy integrals as aggregation operators. Proc. of 4th International Intuitionistic Fuzzy Sets and Contemporary Mathematics Conference, Turkey, 95–99.
  16. Melin, P., Sánchez, D., & Castillo, O. (2012). Genetic optimization of modular neural networks with fuzzy response integration for human recognition. Inf. Sci., 197, 1–19.
  17. Mendez-Vazquez, A., Gader, P., Keller, J. M., & Chamberlin, K. (2008). Minimum classification error training for Choquet integrals with applications to landmine detection, IEEE Trans. Fuzzy Syst. 16 (1), 225–238.
  18. Mendoza, O., Melin, P., & Licea, G. (2009). Interval type-2 fuzzy logic for edges detection in digital images. Int. J. Intell. Syst. 24(11), 1115–1133.
  19. Mendoza, O., Melin, P., & Licea, G. (2008). Interval Type-2 Fuzzy Logic for Module Relevance Estimation in Sugeno Integration of Modular Neural Networks. Soft Computing for Hybrid Intelligent Systems, Studies in Computational Intelligence, Vol. 154, Springer, 115–127.
  20. Sánchez, D., Melin, P., & Castillo, O. (2017). Optimization of modular granular neural networks using a firefly algorithm for human recognition. Eng. Appl. of AI 64, 172–186.
  21. Štajner-Papuga, I., Lozanov-Crvenković, Z. & Grujić, G. (2016). On Sugeno integral based mean value for fuzzy quantities, Proc. of IEEE 14th International Symposium on Intelligent Systems and Informatics (SISY), Subotica, 155–160.
  22. Sugeno, M. (1974). Theory of Fuzzy Integrals and Its Applications. Doctoral Thesis, Tokyo Institute of Technology.
  23. Torra, V., & Narukawa, Y. (2007). Modeling Decisions, Information Fusion and Aggregation Operators. Heidelberg: Springer-Verlag.
  24. Verikas, A., Lipnickas, A., Malmqvist, K., Bacauskiene, M., & Gelzinis, A. (1999). Soft combination of neural classifiers: A comparative study, Pattern Recognition Letters, 20 (4), 429–444.
  25. Yager, R. (2008). A knowledge-based approach to adversarial decision-making Int. J. Intell. Syst., 23 (1), 1–21.
  26. Zadeh, L. A. (1965). Fuzzy sets. Inform Control, 8, 338–353

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