An Application for Medical Diagnosis Using Correlation Coefficient With Modal Operators and Operator Identifying and Unary
Resumo
This paper aims to study the Atanassov's correlation coefficient (A-CC) between two of Atanassov's intuitionistic fuzzy sets (A-IFS), obtained as images of intuitionistic fuzzy modal operators. The composition of modal operators are investigated, verifying under which conditions an A-CC preserves the main properties related to conjugate and complement operations performed on A-IFS. In addition, a simulation based on the proposal methodology using operators described above is applied to a medical diagnosis analysis.
Palavras-chave:
Correlation coefficient, modal operators, medical diagnosis, decision making
Referências
Atanassov, K. (1983). Intuitionistic fuzzy sets. vii itkr session. sofia. Centr. Sci.-Techn. Library of Bulg. Acad. of Sci., 1697(84).
Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets Systems, 20(1):87–96.
Atanassov, K. T. (1999). Intuitionistic Fuzzy Sets: Theory and Applications. Studies in Fuzziness and Soft Computing. Physica-Verlag HD, Heidelberg, Germany, Germany.
Atanassov, K. T. (2012). On Intuitionistic Fuzzy Sets Theory, volume 283 of Studies in Fuzziness and Soft Computing. Springer.
Bertei, A. and Reiser, R. (2018). Correlation coefficient analysis performed on duality and conjugate modal-level operators. In 2018 IEEE International Conference on Fuzzy Systems.
Bertei, A. and Reiser, R. (2019). Correlation coefficient of modal level operators: An application to medical diagnosis. In 11th International Joint Conference on Computational Intelligence (FCTA 2019), pages 1–9.
Bertei, A., Reiser, R. H. S., and Foss, L. (2021). Correlation Analysis Via Intuitionistic Fuzzy Modal and Aggregation Operators, pages 163–190. Springer International Publishing, Cham.
Bertei, A., Zanotelli, R., Cardoso, W., Reiser, R., Foss, L., and Bedregal, B. (2016). Correlation coefficient analysis based on fuzzy negations and representable automorphisms. In 2016 IEEE International Conference on Fuzzy Systems, pages 127–132.
Bustince, H., Barrenechea, E., and Mohedano, V. (2004). Intuicionistic fuzzy implication operators - an expression and main properties. Uncertainty, Fuzziness and KnowledgeBased Systems, 12:387–406.
Bustince, H., Burillo, P., and Soria, F. (2003). Automorphisms, negations and implication operators. Fuzzy Sets and Systems, 134(2):209 – 229.
Bustince, H., Kacprzyk, J., and Mohedanoi, V. (2000). Tntuitionistic fuzzy generators, application to intuitionistic fuzzy complementation. Fuzzy Sets and Syst, 114:485–504.
Dencheva, K. (2004). Extension of intuitionistic fuzzy modal operators () and (). In II International IEEE Conference, volume 3, pages 21–22.
Deschrijver, G., Cornelis, C., and Kerre, E. (2004). On the representation of intuitionistic fuzzy t-norms and t-conorms. IEEE Transactions Fuzzy Systems, 1(12):45–61.
Meng, F., Wang, C., Chen, X., and Zhang, Q. (2016). Correlation coefficients of intervalvalued hesitant fuzzy sets and their application based on the shapley function. International Journal of Intelligence Systems, 31(1):17–43.
Reiser, R., Visintin, L., Benítez, I., and Bedregal, B. (2013). Correlations from conjugate and dual intuitionistic fuzzy triangular norms and conorms. In 2013 Joint IFSA World Congress and NAFIPS Annual Meeting, pages 1394–1399.
Szmidt, E. and Kacprzyk, J. (2012). A new approach to principal component analysis for intuitionistic fuzzy data sets. In et al., S. G., editor, CCIS – IPMU 2012, volume 298, pages 529–538. Springer.
Szmidt, E., Kacprzyk, J., and Bujnowski, P. (2012). Correlation between intuitionistic fuzzy sets: Some conceptual and numerical extensions. In 2012 IEEE International Conference on Fuzzy Systems. p.1-7.
Xu, Z. (2006). On correlation measures of intuitionistic fuzzy sets. In Intelligence Data Engineering and Automated Learning – IDEAL 2006, pages 16–24.
Zadeh, L. (1965). Fuzzy sets. Information Control, 8(3):338–353.
Zadeh, L. (1975). The concept of a linguistic variable and its application to approximate reasoning. Information Sciences, 8(3):199 – 249.
Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets Systems, 20(1):87–96.
Atanassov, K. T. (1999). Intuitionistic Fuzzy Sets: Theory and Applications. Studies in Fuzziness and Soft Computing. Physica-Verlag HD, Heidelberg, Germany, Germany.
Atanassov, K. T. (2012). On Intuitionistic Fuzzy Sets Theory, volume 283 of Studies in Fuzziness and Soft Computing. Springer.
Bertei, A. and Reiser, R. (2018). Correlation coefficient analysis performed on duality and conjugate modal-level operators. In 2018 IEEE International Conference on Fuzzy Systems.
Bertei, A. and Reiser, R. (2019). Correlation coefficient of modal level operators: An application to medical diagnosis. In 11th International Joint Conference on Computational Intelligence (FCTA 2019), pages 1–9.
Bertei, A., Reiser, R. H. S., and Foss, L. (2021). Correlation Analysis Via Intuitionistic Fuzzy Modal and Aggregation Operators, pages 163–190. Springer International Publishing, Cham.
Bertei, A., Zanotelli, R., Cardoso, W., Reiser, R., Foss, L., and Bedregal, B. (2016). Correlation coefficient analysis based on fuzzy negations and representable automorphisms. In 2016 IEEE International Conference on Fuzzy Systems, pages 127–132.
Bustince, H., Barrenechea, E., and Mohedano, V. (2004). Intuicionistic fuzzy implication operators - an expression and main properties. Uncertainty, Fuzziness and KnowledgeBased Systems, 12:387–406.
Bustince, H., Burillo, P., and Soria, F. (2003). Automorphisms, negations and implication operators. Fuzzy Sets and Systems, 134(2):209 – 229.
Bustince, H., Kacprzyk, J., and Mohedanoi, V. (2000). Tntuitionistic fuzzy generators, application to intuitionistic fuzzy complementation. Fuzzy Sets and Syst, 114:485–504.
Dencheva, K. (2004). Extension of intuitionistic fuzzy modal operators () and (). In II International IEEE Conference, volume 3, pages 21–22.
Deschrijver, G., Cornelis, C., and Kerre, E. (2004). On the representation of intuitionistic fuzzy t-norms and t-conorms. IEEE Transactions Fuzzy Systems, 1(12):45–61.
Meng, F., Wang, C., Chen, X., and Zhang, Q. (2016). Correlation coefficients of intervalvalued hesitant fuzzy sets and their application based on the shapley function. International Journal of Intelligence Systems, 31(1):17–43.
Reiser, R., Visintin, L., Benítez, I., and Bedregal, B. (2013). Correlations from conjugate and dual intuitionistic fuzzy triangular norms and conorms. In 2013 Joint IFSA World Congress and NAFIPS Annual Meeting, pages 1394–1399.
Szmidt, E. and Kacprzyk, J. (2012). A new approach to principal component analysis for intuitionistic fuzzy data sets. In et al., S. G., editor, CCIS – IPMU 2012, volume 298, pages 529–538. Springer.
Szmidt, E., Kacprzyk, J., and Bujnowski, P. (2012). Correlation between intuitionistic fuzzy sets: Some conceptual and numerical extensions. In 2012 IEEE International Conference on Fuzzy Systems. p.1-7.
Xu, Z. (2006). On correlation measures of intuitionistic fuzzy sets. In Intelligence Data Engineering and Automated Learning – IDEAL 2006, pages 16–24.
Zadeh, L. (1965). Fuzzy sets. Information Control, 8(3):338–353.
Zadeh, L. (1975). The concept of a linguistic variable and its application to approximate reasoning. Information Sciences, 8(3):199 – 249.
Publicado
17/11/2021
Como Citar
BERTEI, Alex; REISER, Renata H. S.; FOSS, Luciana.
An Application for Medical Diagnosis Using Correlation Coefficient With Modal Operators and Operator Identifying and Unary. In: WORKSHOP-ESCOLA DE INFORMÁTICA TEÓRICA (WEIT), 6. , 2021, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 33-40.
DOI: https://doi.org/10.5753/weit.2021.18919.
