A Self-Tuning ensemble approach for drift detection
Resumo
Processing data streams is challenging due to the need for mining algorithms to adapt to real-time drifts. Ensemble strategies for concept drift detection show promise, yet gaps in flexibility and detection remain. We propose the Self-tuning Drift Ensemble (StDE) method, which dynamically adapts ensemble structure to stream changes while maintaining a lightweight solution. StDE adjusts the number of base learners through a self-regulating voting system, achieving high detection accuracy. Experiments across various drift scenarios demonstrate the superior performance of our method compared to established baselines.
Palavras-chave:
Data Stream, Drift, Ensemble
Referências
Abbasi, A., Javed, A. R., Chakraborty, C., Nebhen, J., Zehra, W., and Jalil, Z. (2021). Elstream: An ensemble learning approach for concept drift detection in dynamic social big data stream learning. IEEE Access, 9:66408–66419.
Cano, A. and Krawczyk, B. (2022). Rose: robust online self-adjusting ensemble for continual learning on imbalanced drifting data streams. Machine Learning, 111(7):2561–2599.
Deckert, M. (2011). Batch weighted ensemble for mining data streams with concept drift. In Foundations of Intelligent Systems: 19th International Symposium, ISMIS 2011, Warsaw, Poland, June 28-30, 2011. Proceedings 19, pages 290–299. Springer.
DeVries, Z., Locke, E., Hoda, M., Moravek, D., Phan, K., Stratton, A., Kingwell, S., Wai, E. K., and Phan, P. (2021). Using a national surgical database to predict complications following posterior lumbar surgery and comparing the area under the curve and f1-score for the assessment of prognostic capability. The Spine Journal, 21(7):1135–1142.
Du, L., Song, Q., Zhu, L., and Zhu, X. (2015). A selective detector ensemble for concept drift detection. The Computer Journal, 58(3):457–471.
Han, M., Chen, Z., Li, M., Wu, H., and Zhang, X. (2022). A survey of active and passive concept drift handling methods. Computational Intelligence, 38(4):1492–1535.
Khamassi, I., Sayed-Mouchaweh, M., Hammami, M., and Ghédira, K. (2013). Ensemble classifiers for drift detection and monitoring in dynamical environments. In Annual Conference of the PHM Society, volume 5.
Komorniczak, J., Zyblewski, P., and Ksieniewicz, P. (2022). Statistical drift detection ensemble for batch processing of data streams. Knowledge-Based Systems, 252:109380.
Korycki, L. and Krawczyk, B. (2019). Unsupervised drift detector ensembles for data stream mining. In 2019 IEEE international conference on data science and advanced analytics (DSAA), pages 317–325. IEEE.
Lagman, A., Alfonso, L., Goh, M., Lalata, J.-A., Magcuyao, J. P., and Vicente, H. (2020). Classification algorithm accuracy improvement for student graduation prediction using ensemble model. International Journal of Information and Education Technology, 10:723–727.
Martins, V. E., Cano, A., and Junior, S. B. (2023). Meta-learning for dynamic tuning of active learning on stream classification. Pattern Recognition, 138:109359.
Mavromatis, I., Sanchez-Mompo, A., Raimondo, F., Pope, J., Bullo, M., Weeks, I., Kumar, V., Carnelli, P., Oikonomou, G., Spyridopoulos, T., et al. (2023). Le3d: a lightweight ensemble framework of data drift detectors for resource-constrained devices. In 2023 IEEE 20th Consumer Communications & Networking Conference (CCNC), pages 611–619. IEEE.
Pérez, J. L. M., Barros, R. S., and Santos, S. G. (2020). Statistical tests ensemble drift detector. In 2020 IEEE Symposium Series on Computational Intelligence (SSCI), pages 1021–1028. IEEE.
Ramane, M. and Gnanasekar, J. (2022). A novel hybrid concept drift detector ensemble for handling pure and hybrid drift in non-stationary data streams. NeuroQuantology, 20(8):6170.
Sakurai, G. Y., Lopes, J. F., Zarpelão, B. B., and Barbon Junior, S. (2023). Benchmarking change detector algorithms from different concept drift perspectives. Future Internet, 15(5).
Samant, R. C., Patil, S. H., Sinha, R. N., and Kadam, A. K. A systematic ensemble approach for concept drift detector selection in data stream classifiers.
Silva, R. P., Junior, S. B., Zarpelão, B. B., and De Melo, L. F. (2024). Unsupervised tuning for drift detectors using change detector segmentation. IEEE Access, 12:54256–54271.
Cano, A. and Krawczyk, B. (2022). Rose: robust online self-adjusting ensemble for continual learning on imbalanced drifting data streams. Machine Learning, 111(7):2561–2599.
Deckert, M. (2011). Batch weighted ensemble for mining data streams with concept drift. In Foundations of Intelligent Systems: 19th International Symposium, ISMIS 2011, Warsaw, Poland, June 28-30, 2011. Proceedings 19, pages 290–299. Springer.
DeVries, Z., Locke, E., Hoda, M., Moravek, D., Phan, K., Stratton, A., Kingwell, S., Wai, E. K., and Phan, P. (2021). Using a national surgical database to predict complications following posterior lumbar surgery and comparing the area under the curve and f1-score for the assessment of prognostic capability. The Spine Journal, 21(7):1135–1142.
Du, L., Song, Q., Zhu, L., and Zhu, X. (2015). A selective detector ensemble for concept drift detection. The Computer Journal, 58(3):457–471.
Han, M., Chen, Z., Li, M., Wu, H., and Zhang, X. (2022). A survey of active and passive concept drift handling methods. Computational Intelligence, 38(4):1492–1535.
Khamassi, I., Sayed-Mouchaweh, M., Hammami, M., and Ghédira, K. (2013). Ensemble classifiers for drift detection and monitoring in dynamical environments. In Annual Conference of the PHM Society, volume 5.
Komorniczak, J., Zyblewski, P., and Ksieniewicz, P. (2022). Statistical drift detection ensemble for batch processing of data streams. Knowledge-Based Systems, 252:109380.
Korycki, L. and Krawczyk, B. (2019). Unsupervised drift detector ensembles for data stream mining. In 2019 IEEE international conference on data science and advanced analytics (DSAA), pages 317–325. IEEE.
Lagman, A., Alfonso, L., Goh, M., Lalata, J.-A., Magcuyao, J. P., and Vicente, H. (2020). Classification algorithm accuracy improvement for student graduation prediction using ensemble model. International Journal of Information and Education Technology, 10:723–727.
Martins, V. E., Cano, A., and Junior, S. B. (2023). Meta-learning for dynamic tuning of active learning on stream classification. Pattern Recognition, 138:109359.
Mavromatis, I., Sanchez-Mompo, A., Raimondo, F., Pope, J., Bullo, M., Weeks, I., Kumar, V., Carnelli, P., Oikonomou, G., Spyridopoulos, T., et al. (2023). Le3d: a lightweight ensemble framework of data drift detectors for resource-constrained devices. In 2023 IEEE 20th Consumer Communications & Networking Conference (CCNC), pages 611–619. IEEE.
Pérez, J. L. M., Barros, R. S., and Santos, S. G. (2020). Statistical tests ensemble drift detector. In 2020 IEEE Symposium Series on Computational Intelligence (SSCI), pages 1021–1028. IEEE.
Ramane, M. and Gnanasekar, J. (2022). A novel hybrid concept drift detector ensemble for handling pure and hybrid drift in non-stationary data streams. NeuroQuantology, 20(8):6170.
Sakurai, G. Y., Lopes, J. F., Zarpelão, B. B., and Barbon Junior, S. (2023). Benchmarking change detector algorithms from different concept drift perspectives. Future Internet, 15(5).
Samant, R. C., Patil, S. H., Sinha, R. N., and Kadam, A. K. A systematic ensemble approach for concept drift detector selection in data stream classifiers.
Silva, R. P., Junior, S. B., Zarpelão, B. B., and De Melo, L. F. (2024). Unsupervised tuning for drift detectors using change detector segmentation. IEEE Access, 12:54256–54271.
Publicado
17/11/2024
Como Citar
SAKURAI, Guilherme Y.; ZARPELÃO, Bruno B.; BARBON JUNIOR, Sylvio.
A Self-Tuning ensemble approach for drift detection. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 21. , 2024, Belém/PA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 811-822.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2024.245158.
