Improving Fuzzy Rule-Based Classifier Systems Considering Data Streams and Concept Drift
Resumo
Neste trabalho é avaliado o desempenho de um Sistema Classificador Baseado em Regras Fuzzy considerando fluxo de dados e concept drift. O métod Hoeffding Drift Detection Method é aplicado para detectar as mudanças de conceito no fluxo de dados, as quais podem ser de dois tipos, gradual e abrupto. Após a detecção do concept drift, três cenários foram avaliados para o ajuste das regras do sistema. No primeiro, uma nova base de regras é gerada. No segundo, um valor de utilidade é calculado para as regras, e as que tiverem utilidade abaixo dessa média são descartadas. Após isso, novas regras são criadas utilizando os dados atuais. Por fim, no terceiro cenário, novas regras são geradas, e em seguida, a utilidade é calculada. As regras, novas e antigas, que tenham utilidade menor do que a média são descartadas. Experimentos foram executados nos três cenários considerando datasets artificiais e reais, variando o tamanho da janela de dados entre os conceitos em 100 e 1000 exemplos. Os resultados mostram que os cenários 1 e 2 ajustaram melhor o sistema durante a mudança dos conceitos, tanto para os dados artificiais quanto para os reais. Por fim, o teste estatístico de Friedman foi aplicado e constatou que os resultados são significativos, com exceção de apenas dois casos.Referências
Agrahari, S. and Singh, A. K. (2022). Concept drift detection in data stream mining: A literature review. Journal of King Saud University-Computer and Information Sciences, 34(10):9523–9540.
Alshalali, T. and Josyula, D. (2020). Evaluating extreme learning machine models in the presence of concept drift in streaming data. In Proceedings of the 2020 the 4th International Conference on Information System and Data Mining, pages 107–112.
Angelov, P. and Zhou, X. (2008). On line learning fuzzy rule-based system structure from data streams. In 2008 IEEE international conference on fuzzy systems (IEEE world congress on computational intelligence), pages 915–922. IEEE.
Bifet, A., Holmes, G., Pfahringer, B., Kirkby, R., and Gavalda, R. (2009). New ensemble methods for evolving data streams. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 139–148.
Brzezinski, D. and Stefanowski, J. (2013). Reacting to different types of concept drift: The accuracy updated ensemble algorithm. IEEE transactions on neural networks and learning systems, 25(1):81–94.
Chagas, P., Souza, L., Araújo, I., Aldeman, N., Duarte, A., Angelo, M., dos Santos, W. L., and Oliveira, L. (2020). Classification of glomerular hypercellularity using convolutional features and support vector machine. Artificial Intelligence in Medicine, 103:101808.
Charizanos, G., Demirhan, H., and İçen, D. (2024). An online fuzzy fraud detection framework for credit card transactions. Expert Systems with Applications, 252:124127.
Emam, M. M., Houssein, E. H., Samee, N. A., Alohali, M. A., and Hosney, M. E. (2024). Breast cancer diagnosis using optimized deep convolutional neural network based on transfer learning technique and improved coati optimization algorithm. Expert Systems with Applications, 255:124581.
Fawcett, T. (2006). An introduction to roc analysis. Pattern Recognition Letters, 27(8):861–874. ROC Analysis in Pattern Recognition.
Frias-Blanco, I., del Campo-Ávila, J., Ramos-Jimenez, G., Morales-Bueno, R., Ortiz-Diaz, A., and Caballero-Mota, Y. (2014). Online and non-parametric drift detection methods based on hoeffding’s bounds. IEEE Transactions on Knowledge and Data Engineering, 27(3):810–823.
Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., and Bouchachia, A. (2014). A survey on concept drift adaptation. ACM computing surveys (CSUR), 46(4):1–37.
Gonçalves Júnior, P. M. (2013). Multivariate Non-Parametric Statistical Tests to Reuse Classifiers in Recurring Concept Drifting Environments. PhD thesis, Universidade Federal de Pernambuco, Recife, Brasil.
Lu, J., Ma, G., and Zhang, G. (2024). Fuzzy machine learning: A comprehensive framework and systematic review. IEEE Transactions on Fuzzy Systems, 32(7):3861–3878.
Shahparast, H., Jahromi, M. Z., Taheri, M., and Hamzeloo, S. (2014). A novel weight adjustment method for handling concept-drift in data stream classification. Arabian Journal for Science and Engineering, 39:799–807.
Sun, Y., Tang, K., Zhu, Z., and Yao, X. (2018). Concept drift adaptation by exploiting historical knowledge. IEEE transactions on neural networks and learning systems, 29(10):4822–4832.
Tan, Q., Fu, M., Wang, Z., Yuan, H., and Sun, J. (2024). A real-time early warning classification method for natural gas leakage based on random forest. Reliability Engineering & System Safety, 251:110372.
University of Waikato (2025). MOA Datasets. [link].
Varshney, A. K. and Torra, V. (2023). Literature review of the recent trends and applications in various fuzzy rule-based systems. International Journal of Fuzzy Systems, 25(6):2163–2186.
Wang, L.-X. and Mendel, J. (1992). Generating fuzzy rules by learning from examples. IEEE Transactions on Systems, Man, and Cybernetics, 22(6):1414–1427.
Alshalali, T. and Josyula, D. (2020). Evaluating extreme learning machine models in the presence of concept drift in streaming data. In Proceedings of the 2020 the 4th International Conference on Information System and Data Mining, pages 107–112.
Angelov, P. and Zhou, X. (2008). On line learning fuzzy rule-based system structure from data streams. In 2008 IEEE international conference on fuzzy systems (IEEE world congress on computational intelligence), pages 915–922. IEEE.
Bifet, A., Holmes, G., Pfahringer, B., Kirkby, R., and Gavalda, R. (2009). New ensemble methods for evolving data streams. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 139–148.
Brzezinski, D. and Stefanowski, J. (2013). Reacting to different types of concept drift: The accuracy updated ensemble algorithm. IEEE transactions on neural networks and learning systems, 25(1):81–94.
Chagas, P., Souza, L., Araújo, I., Aldeman, N., Duarte, A., Angelo, M., dos Santos, W. L., and Oliveira, L. (2020). Classification of glomerular hypercellularity using convolutional features and support vector machine. Artificial Intelligence in Medicine, 103:101808.
Charizanos, G., Demirhan, H., and İçen, D. (2024). An online fuzzy fraud detection framework for credit card transactions. Expert Systems with Applications, 252:124127.
Emam, M. M., Houssein, E. H., Samee, N. A., Alohali, M. A., and Hosney, M. E. (2024). Breast cancer diagnosis using optimized deep convolutional neural network based on transfer learning technique and improved coati optimization algorithm. Expert Systems with Applications, 255:124581.
Fawcett, T. (2006). An introduction to roc analysis. Pattern Recognition Letters, 27(8):861–874. ROC Analysis in Pattern Recognition.
Frias-Blanco, I., del Campo-Ávila, J., Ramos-Jimenez, G., Morales-Bueno, R., Ortiz-Diaz, A., and Caballero-Mota, Y. (2014). Online and non-parametric drift detection methods based on hoeffding’s bounds. IEEE Transactions on Knowledge and Data Engineering, 27(3):810–823.
Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., and Bouchachia, A. (2014). A survey on concept drift adaptation. ACM computing surveys (CSUR), 46(4):1–37.
Gonçalves Júnior, P. M. (2013). Multivariate Non-Parametric Statistical Tests to Reuse Classifiers in Recurring Concept Drifting Environments. PhD thesis, Universidade Federal de Pernambuco, Recife, Brasil.
Lu, J., Ma, G., and Zhang, G. (2024). Fuzzy machine learning: A comprehensive framework and systematic review. IEEE Transactions on Fuzzy Systems, 32(7):3861–3878.
Shahparast, H., Jahromi, M. Z., Taheri, M., and Hamzeloo, S. (2014). A novel weight adjustment method for handling concept-drift in data stream classification. Arabian Journal for Science and Engineering, 39:799–807.
Sun, Y., Tang, K., Zhu, Z., and Yao, X. (2018). Concept drift adaptation by exploiting historical knowledge. IEEE transactions on neural networks and learning systems, 29(10):4822–4832.
Tan, Q., Fu, M., Wang, Z., Yuan, H., and Sun, J. (2024). A real-time early warning classification method for natural gas leakage based on random forest. Reliability Engineering & System Safety, 251:110372.
University of Waikato (2025). MOA Datasets. [link].
Varshney, A. K. and Torra, V. (2023). Literature review of the recent trends and applications in various fuzzy rule-based systems. International Journal of Fuzzy Systems, 25(6):2163–2186.
Wang, L.-X. and Mendel, J. (1992). Generating fuzzy rules by learning from examples. IEEE Transactions on Systems, Man, and Cybernetics, 22(6):1414–1427.
Publicado
29/09/2025
Como Citar
LEITE JUNIOR, Vanderleicio Carvalho; PIRES, Matheus Giovanni.
Improving Fuzzy Rule-Based Classifier Systems Considering Data Streams and Concept Drift. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 22. , 2025, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 1431-1442.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2025.12289.
