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Görüntü bulanık kümelerde altkümelik ve çok kriterli karar vermeye uygulanması

Year 2022, Volume: 12 Issue: 2, 385 - 394, 15.04.2022
https://doi.org/10.17714/gumusfenbil.974420

Abstract

Görüntü bulanık küme, sezgisel bulanık kümenin doğrudan bir genellemesidir ve bu nedenle gerçek hayat problemleri üzerinde çalışırken belirsizlikle başa çıkma konusunda daha yeteneklidir. Kapsama kavramı, bulanık kümeler ailesinde sıklıkla çalışılan ve gerçek hayat problemlerinde birçok uygulaması olan bir konudur. Bu nedenle, bu çalışmada, görüntü bulanık kümeleri arasındaki kapsama derecesinin ölçülmesi tanıtılmıştır. Bu amaçla, önce altkümelik ölçüsü için aksiyomlar verilmiş, ardından görüntü bulanık kümeleri için uzaklık ölçüsüne dayalı bir altküme ölçüsü önerilmiştir. Sonra, verilen ölçünün uygulanabilirliğini ve kullanışlılığını göstermek için sayısal bir örnek verilmiştir. Son olarak, sonuçlar PFS için altkümelik ölçüsünün geçerliliğini göstermek için mevcut yöntemler ve ortalama operatörleri ile karşılaştırılmıştır.

References

  • Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87–96.
  • Cornelis, C., & Kerre, E. (2003). Inclusion measures in intuitionistic fuzzy set theory. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 2711, 345–356. https://doi.org/10.1007/978-3-540-45062-7_28
  • Cornelis, C., Van der Donck, C., & Kerre, E. (2003). Sinha-Dougherty approach to the fuzzification of set inclusion revisited. Fuzzy Sets and Systems, 134(2), 283–295. https://doi.org/10.1016/S0165-0114(02)00225-7
  • Cường, B. C. (2015). Picture fuzzy sets. Journal of Computer Science and Cybernetics, 30(4), 409–420. https://doi.org/10.15625/1813-9663/30/4/5032
  • Cuong, B. C., & Kreinovich, V. (2014). Picture fuzzy sets - A new concept for computational intelligence problems. 2013 3rd World Congress on Information and Communication Technologies, WICT 2013, 1–6. https://doi.org/10.1109/WICT.2013.7113099
  • Ganie, A. H., Singh, S., & Bhatia, P. K. (2020). Some new correlation coefficients of picture fuzzy sets with applications. Neural Computing and Applications, 32(16), 12609–12625. https://doi.org/10.1007/s00521-020-04715-y
  • Grzegorzewski, P., & Mrówka, E. (2004). Subsethood measure for intuitionistic fuzzy sets. IEEE International Conference on Fuzzy Systems, 1, 139–142. https://doi.org/10.1109/fuzzy.2004.1375704
  • Köseoğlu, A. (2021). Tourism Management Application in Pythagorean Fuzzy Sets With COPRAS Method. 7th Ifs And Contemporary Mathematics Conference, 197–208.
  • Köseoğlu, A., & Şahin, R. (2019). An Intuitionistic Multiplicative TOPSIS Method for a Supplier Selection Problem. 3rd International Conference on Advanced Engineering Technologies (ICADET’19), 1076–1082.
  • Pękala, B., Bentkowska, U., Sesma-Sara, M., Fernandez, J., Lafuente, J., Altalhi, A., Knap, M., Bustince, H., & Pintor, J. M. (2020). Interval subsethood measures with respect to uncertainty for the interval-valued fuzzy setting. International Journal of Computational Intelligence Systems, 13(1), 167–177. https://doi.org/10.2991/ijcis.d.200204.001
  • Peng, X., Yuan, H., & Yang, Y. (2017). Pythagorean Fuzzy Information Measures and Their Applications. International Journal of Intelligent Systems, 32(10), 991–1029. https://doi.org/10.1002/int.21880
  • Şahin, R, & Küçük, A. (2015). Subsethood measure for single valued neutrosophic sets. Journal of Intelligent and Fuzzy Systems, 29(2), 525–530. https://doi.org/10.3233/IFS-141304
  • Şahin, R, Karabacak, M., Sahin, R., & Karabacak, M. (2015). A multi attribute decision making method based on inclusion measure for interval neutrosophic sets. International Journal of Advances in Engineering Sciences and Applied Mathematics, 2(2), 13–15. www.ijeas.org
  • Singh, P. (2015). Correlation coefficients for picture fuzzy sets. Journal of Intelligent and Fuzzy Systems, 28(2), 591–604. https://doi.org/10.3233/IFS-141338
  • Sinha, D., & Dougherty, E. R. (1993). Fuzzification of set inclusion: Theory and applications. Fuzzy Sets and Systems, 55(1), 15–42. https://doi.org/10.1016/0165-0114(93)90299-W
  • Son, L. H. (2016). Generalized picture distance measure and applications to picture fuzzy clustering. Applied Soft Computing Journal, 46(C), 284–295. https://doi.org/10.1016/j.asoc.2016.05.009
  • Thao, N. X. (2020). Similarity measures of picture fuzzy sets based on entropy and their application in MCDM. Pattern Analysis and Applications, 23(3), 1203–1213. https://doi.org/10.1007/s10044-019-00861-9
  • Wang, C., Zhou, X., Tu, H., & Tao, S. (2017). Some geometric aggregation operators based on picture fuzzy sets and their application in multiple attribute decision making. Italian Journal of Pure and Applied Mathematics, 37(37), 477–492.
  • Wei, G. (2017). Picture fuzzy aggregation operators and their application to multiple attribute decision making. Journal of Intelligent and Fuzzy Systems, 33(2), 713–724. https://doi.org/10.3233/JIFS-161798
  • Wei, G. (2018). Some similarity measures for picture fuzzy sets and their applications. Iranian Journal of Fuzzy Systems, 15(1), 77–89. https://doi.org/10.22111/ijfs.2018.3579
  • Ye, J. (2010). Fuzzy decision-making method based on the weighted correlation coefficient under intuitionistic fuzzy environment. European Journal of Operational Research, 205(1), 202–204. https://doi.org/10.1016/j.ejor.2010.01.019
  • Young, V. R. (1996). Fuzzy subsethood. Fuzzy Sets and Systems, 77(3), 371–384. https://doi.org/10.1016/0165-0114(95)00045-3
  • Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://www.sciencedirect.com/science/article/pii/S001999586590241X
  • Zadrożny, S., Kacprzyk, J., Gajewski, M., & De Tré, G. (2021). On the Use of Fuzzy Sets Weighted Subsethood Indicators in a Text Categorization Problem. Advances in Intelligent Systems and Computing, 1081 AISC, 341–362. https://doi.org/10.1007/978-3-030-47024-1_33

Subsethood measure for picture fuzzy sets and its applications on multicriteria decision making

Year 2022, Volume: 12 Issue: 2, 385 - 394, 15.04.2022
https://doi.org/10.17714/gumusfenbil.974420

Abstract

Picture fuzzy set is a direct generalization of intuitionistic fuzzy set and is therefore more capable of dealing with uncertainty while working on real life problems. The concept of inclusion is a subject that is frequently studied in family of fuzzy sets and has many applications in real life problems. Therefore, in this work, the measuring degree of inclusion between picture fuzzy sets is introduced. For this purpose, firstly axioms for subsethood measure are given and then a subsethood measure based on a distance measure for picture fuzzy sets is proposed. Then, a numerical example is provided to illustrate the applicability and usefulness of the presented measure. Finally, results are compared with the existing methods and aggregation operator to show validity of subsethood measure for PFS.

References

  • Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87–96.
  • Cornelis, C., & Kerre, E. (2003). Inclusion measures in intuitionistic fuzzy set theory. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 2711, 345–356. https://doi.org/10.1007/978-3-540-45062-7_28
  • Cornelis, C., Van der Donck, C., & Kerre, E. (2003). Sinha-Dougherty approach to the fuzzification of set inclusion revisited. Fuzzy Sets and Systems, 134(2), 283–295. https://doi.org/10.1016/S0165-0114(02)00225-7
  • Cường, B. C. (2015). Picture fuzzy sets. Journal of Computer Science and Cybernetics, 30(4), 409–420. https://doi.org/10.15625/1813-9663/30/4/5032
  • Cuong, B. C., & Kreinovich, V. (2014). Picture fuzzy sets - A new concept for computational intelligence problems. 2013 3rd World Congress on Information and Communication Technologies, WICT 2013, 1–6. https://doi.org/10.1109/WICT.2013.7113099
  • Ganie, A. H., Singh, S., & Bhatia, P. K. (2020). Some new correlation coefficients of picture fuzzy sets with applications. Neural Computing and Applications, 32(16), 12609–12625. https://doi.org/10.1007/s00521-020-04715-y
  • Grzegorzewski, P., & Mrówka, E. (2004). Subsethood measure for intuitionistic fuzzy sets. IEEE International Conference on Fuzzy Systems, 1, 139–142. https://doi.org/10.1109/fuzzy.2004.1375704
  • Köseoğlu, A. (2021). Tourism Management Application in Pythagorean Fuzzy Sets With COPRAS Method. 7th Ifs And Contemporary Mathematics Conference, 197–208.
  • Köseoğlu, A., & Şahin, R. (2019). An Intuitionistic Multiplicative TOPSIS Method for a Supplier Selection Problem. 3rd International Conference on Advanced Engineering Technologies (ICADET’19), 1076–1082.
  • Pękala, B., Bentkowska, U., Sesma-Sara, M., Fernandez, J., Lafuente, J., Altalhi, A., Knap, M., Bustince, H., & Pintor, J. M. (2020). Interval subsethood measures with respect to uncertainty for the interval-valued fuzzy setting. International Journal of Computational Intelligence Systems, 13(1), 167–177. https://doi.org/10.2991/ijcis.d.200204.001
  • Peng, X., Yuan, H., & Yang, Y. (2017). Pythagorean Fuzzy Information Measures and Their Applications. International Journal of Intelligent Systems, 32(10), 991–1029. https://doi.org/10.1002/int.21880
  • Şahin, R, & Küçük, A. (2015). Subsethood measure for single valued neutrosophic sets. Journal of Intelligent and Fuzzy Systems, 29(2), 525–530. https://doi.org/10.3233/IFS-141304
  • Şahin, R, Karabacak, M., Sahin, R., & Karabacak, M. (2015). A multi attribute decision making method based on inclusion measure for interval neutrosophic sets. International Journal of Advances in Engineering Sciences and Applied Mathematics, 2(2), 13–15. www.ijeas.org
  • Singh, P. (2015). Correlation coefficients for picture fuzzy sets. Journal of Intelligent and Fuzzy Systems, 28(2), 591–604. https://doi.org/10.3233/IFS-141338
  • Sinha, D., & Dougherty, E. R. (1993). Fuzzification of set inclusion: Theory and applications. Fuzzy Sets and Systems, 55(1), 15–42. https://doi.org/10.1016/0165-0114(93)90299-W
  • Son, L. H. (2016). Generalized picture distance measure and applications to picture fuzzy clustering. Applied Soft Computing Journal, 46(C), 284–295. https://doi.org/10.1016/j.asoc.2016.05.009
  • Thao, N. X. (2020). Similarity measures of picture fuzzy sets based on entropy and their application in MCDM. Pattern Analysis and Applications, 23(3), 1203–1213. https://doi.org/10.1007/s10044-019-00861-9
  • Wang, C., Zhou, X., Tu, H., & Tao, S. (2017). Some geometric aggregation operators based on picture fuzzy sets and their application in multiple attribute decision making. Italian Journal of Pure and Applied Mathematics, 37(37), 477–492.
  • Wei, G. (2017). Picture fuzzy aggregation operators and their application to multiple attribute decision making. Journal of Intelligent and Fuzzy Systems, 33(2), 713–724. https://doi.org/10.3233/JIFS-161798
  • Wei, G. (2018). Some similarity measures for picture fuzzy sets and their applications. Iranian Journal of Fuzzy Systems, 15(1), 77–89. https://doi.org/10.22111/ijfs.2018.3579
  • Ye, J. (2010). Fuzzy decision-making method based on the weighted correlation coefficient under intuitionistic fuzzy environment. European Journal of Operational Research, 205(1), 202–204. https://doi.org/10.1016/j.ejor.2010.01.019
  • Young, V. R. (1996). Fuzzy subsethood. Fuzzy Sets and Systems, 77(3), 371–384. https://doi.org/10.1016/0165-0114(95)00045-3
  • Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://www.sciencedirect.com/science/article/pii/S001999586590241X
  • Zadrożny, S., Kacprzyk, J., Gajewski, M., & De Tré, G. (2021). On the Use of Fuzzy Sets Weighted Subsethood Indicators in a Text Categorization Problem. Advances in Intelligent Systems and Computing, 1081 AISC, 341–362. https://doi.org/10.1007/978-3-030-47024-1_33
There are 24 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Ali Köseoğlu 0000-0002-2131-7141

Publication Date April 15, 2022
Submission Date July 25, 2021
Acceptance Date January 16, 2022
Published in Issue Year 2022 Volume: 12 Issue: 2

Cite

APA Köseoğlu, A. (2022). Subsethood measure for picture fuzzy sets and its applications on multicriteria decision making. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, 12(2), 385-394. https://doi.org/10.17714/gumusfenbil.974420