Impacto das Generalizações da Integral de Choquet no Modelo ML TSKC FS: Um análise comparativo
Resumo
Este artigo investiga o impacto de diferentes generalizações da Integral de Choquet no desempenho do modelo neuro-fuzzy ML-TSKC FS, voltado para tarefas de classificação multi-rótulo. Foram avaliadas 25 variações do modelo, combinando quatro versões da integral ( CT, CC, CF e CF1F2 ) e a Integral de Choquet Clássica (CO) com cinco medidas fuzzy (uniforme, relativa, produto, potência e ponderada). Os experimentos foram conduzidos em sete bases de dados benchmark com características distintas, utilizando quatro métricas clássicas da literatura: Average Precision (AP), Coverage (CV), Ranking Loss (RL) e Hamming Loss (HL). O objetivo deste estudo é oferecer recomendações práticas para selecionar a melhor combinação entre os pares de Integral de Choquet e medida fuzzy, conforme o critério de desempenho desejado.
Palavras-chave:
generalizações da Integral de Choquet, modelo neuro-fuzzy, medida fuzzy
Referências
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Beliakov, G., Pradera, A., Calvo, T., et al. (2007). Aggregation functions: A guide for practitioners, volume 221. Springer. DOI: 10.1007/978-3-540-73721-6
Bustince, H., Fernández, J., Kolesárová, A., and Mesiar, R. (2015). Directional monotonicity of fusion functions. European Journal of Operational Research, 244(1):300–308. DOI: 10.1016/j.ejor.2015.01.018
Choquet, G. (1954). Theory of capacities. In Annales de l’institut Fourier, volume 5, pages 131–295. [link]
Condori, K. M. (2024). Generalização do processo de agregação do sistema fuzzy multi-rótulo takagi-sugeno-kan baseado na integral de choquet. Master’s thesis, FURG, Rio Grande-RS, Brasil. Dissertação de Mestrado. [link]
Dimuro, G. P., Bedregal, B., Bustince, H., Jurio, A., Baczyński, M., and Miś, K. (2017). Ql-operations and ql-implication functions constructed from tuples (o, g, n) and the generation of fuzzy subsethood and entropy measures. International Journal of Approximate Reasoning, 82:170–192. DOI: 10.1016/j.ijar.2016.12.013
Dimuro, G. P., Fernández, J., Bedregal, B., Mesiar, R., Sanz, J. A., Lucca, G., and Bustince, H. (2020). The state-of-art of the generalizations of the choquet integral: From aggregation and pre-aggregation to ordered directionally monotone functions. Information Fusion, 57:27–43. DOI: 10.1016/j.inffus.2019.10.005
Herrera, F., Charte, F., Rivera, A. J., and Del Jesus, M. J. (2016). Multilabel classification. Springer. DOI: 10.1007/978-3-319-41111-8_2
Jang and Jyh-Shing (1993). ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Transactions on Systems, Man, and Cybernetics. DOI: 10.1109/21.256541
Klement, E. P., Mesiar, R., and Pap, E. (2004). Triangular norms. position paper iii: continuous t-norms. Fuzzy Sets and Systems, 145(3):439–454. DOI: 10.1016/S0165-0114(03)00304-X
Lou, Q., Deng, Z., Xiao, Z., Choi, K.-S., and Wang, S. (2021). Multilabel Takagi-Sugeno-Kang fuzzy system. IEEE Transactions on Fuzzy Systems, 30(9):3410–3425. DOI: 10.1109/TFUZZ.2021.3115967
Lucca, G., Dimuro, G. P., Fernández, J., Bustince, H., Bedregal, B., and Sanz, J. A. (2018a). Improving the performance of fuzzy rule-based classification systems based on a nonaveraging generalization of cc-integrals named c_{F_1F_2}-integrals. IEEE Transactions on Fuzzy Systems, 27(1):124–134. DOI: 10.1109/TFUZZ.2018.2871000
Lucca, G., Sanz, J. A., Dimuro, G. P., Bedregal, B., Asiain, M. J., Elkano, M., and Bustince, H. (2017). Cc-integrals: Choquet-like copula-based aggregation functions and its application in fuzzy rule-based classification systems. Knowledge-Based Systems, 119:32–43. DOI: 10.1016/j.knosys.2016.12.004
Lucca, G., Sanz, J. A., Dimuro, G. P., Bedregal, B., Bustince, H., and Mesiar, R. (2018b). Cf-integrals: A new family of pre-aggregation functions with application to fuzzy rule-based classification systems. Information Sciences, 435:94–110. DOI: 10.1016/j.ins.2017.12.029
Lucca, G., Sanz, J. A., Dimuro, G. P., Bedregal, B., Mesiar, R., Kolesarova, A., and Bustince, H. (2015). Preaggregation functions: Construction and an application. IEEE Transactions on Fuzzy Systems, 24(2):260–272. DOI: 10.1109/TFUZZ.2015.2453020
Murofushi, T., Sugeno, M., and Machida, M. (1994). Non-monotonic fuzzy measures and the choquet integral. Fuzzy sets and Systems, 64(1):73–86. DOI: 10.1016/0165-0114(94)90008-6
Beliakov, G., Pradera, A., Calvo, T., et al. (2007). Aggregation functions: A guide for practitioners, volume 221. Springer. DOI: 10.1007/978-3-540-73721-6
Bustince, H., Fernández, J., Kolesárová, A., and Mesiar, R. (2015). Directional monotonicity of fusion functions. European Journal of Operational Research, 244(1):300–308. DOI: 10.1016/j.ejor.2015.01.018
Choquet, G. (1954). Theory of capacities. In Annales de l’institut Fourier, volume 5, pages 131–295. [link]
Condori, K. M. (2024). Generalização do processo de agregação do sistema fuzzy multi-rótulo takagi-sugeno-kan baseado na integral de choquet. Master’s thesis, FURG, Rio Grande-RS, Brasil. Dissertação de Mestrado. [link]
Dimuro, G. P., Bedregal, B., Bustince, H., Jurio, A., Baczyński, M., and Miś, K. (2017). Ql-operations and ql-implication functions constructed from tuples (o, g, n) and the generation of fuzzy subsethood and entropy measures. International Journal of Approximate Reasoning, 82:170–192. DOI: 10.1016/j.ijar.2016.12.013
Dimuro, G. P., Fernández, J., Bedregal, B., Mesiar, R., Sanz, J. A., Lucca, G., and Bustince, H. (2020). The state-of-art of the generalizations of the choquet integral: From aggregation and pre-aggregation to ordered directionally monotone functions. Information Fusion, 57:27–43. DOI: 10.1016/j.inffus.2019.10.005
Herrera, F., Charte, F., Rivera, A. J., and Del Jesus, M. J. (2016). Multilabel classification. Springer. DOI: 10.1007/978-3-319-41111-8_2
Jang and Jyh-Shing (1993). ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Transactions on Systems, Man, and Cybernetics. DOI: 10.1109/21.256541
Klement, E. P., Mesiar, R., and Pap, E. (2004). Triangular norms. position paper iii: continuous t-norms. Fuzzy Sets and Systems, 145(3):439–454. DOI: 10.1016/S0165-0114(03)00304-X
Lou, Q., Deng, Z., Xiao, Z., Choi, K.-S., and Wang, S. (2021). Multilabel Takagi-Sugeno-Kang fuzzy system. IEEE Transactions on Fuzzy Systems, 30(9):3410–3425. DOI: 10.1109/TFUZZ.2021.3115967
Lucca, G., Dimuro, G. P., Fernández, J., Bustince, H., Bedregal, B., and Sanz, J. A. (2018a). Improving the performance of fuzzy rule-based classification systems based on a nonaveraging generalization of cc-integrals named c_{F_1F_2}-integrals. IEEE Transactions on Fuzzy Systems, 27(1):124–134. DOI: 10.1109/TFUZZ.2018.2871000
Lucca, G., Sanz, J. A., Dimuro, G. P., Bedregal, B., Asiain, M. J., Elkano, M., and Bustince, H. (2017). Cc-integrals: Choquet-like copula-based aggregation functions and its application in fuzzy rule-based classification systems. Knowledge-Based Systems, 119:32–43. DOI: 10.1016/j.knosys.2016.12.004
Lucca, G., Sanz, J. A., Dimuro, G. P., Bedregal, B., Bustince, H., and Mesiar, R. (2018b). Cf-integrals: A new family of pre-aggregation functions with application to fuzzy rule-based classification systems. Information Sciences, 435:94–110. DOI: 10.1016/j.ins.2017.12.029
Lucca, G., Sanz, J. A., Dimuro, G. P., Bedregal, B., Mesiar, R., Kolesarova, A., and Bustince, H. (2015). Preaggregation functions: Construction and an application. IEEE Transactions on Fuzzy Systems, 24(2):260–272. DOI: 10.1109/TFUZZ.2015.2453020
Murofushi, T., Sugeno, M., and Machida, M. (1994). Non-monotonic fuzzy measures and the choquet integral. Fuzzy sets and Systems, 64(1):73–86. DOI: 10.1016/0165-0114(94)90008-6
Publicado
10/09/2025
Como Citar
CONDORI, Karina; DIMURO, Graçaliz; SUAREZ, Julian; SANTOS, Helida; LUCCA, Giancarlo; ASMUS, Tiago.
Impacto das Generalizações da Integral de Choquet no Modelo ML TSKC FS: Um análise comparativo. In: WORKSHOP-ESCOLA DE INFORMÁTICA TEÓRICA (WEIT), 8. , 2025, Ponta Grossa/PR.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
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p. 1-9.
