Abstract
Multiple data streams learning attracts more and more attention recently. Different from learning a single data stream, the uncertain and complex occurrence of concept drift in multiple data streams, bring challenges in real-time learning task. To address this issue, this paper proposed a method called time-warping-based concept drift learning method (TW-CDM) for dealing with multiple data streams. First, a time-warping-based drift identification process is given to recognize the drift region. Second, an augmented learning process is developed by crossly using the located region data. Finally, a selectively augmented learning process is given to reduce the influence of different drift severity. The proposed method is evaluated on both synthetic and real-world datasets, and compared with benchmark methods. The experiment results show the efficiency of the proposed method.
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References
Álvarez, V., Mazuelas, S., Lozano, J.A.: Minimax classification under concept drift with multidimensional adaptation and performance guarantees. In: International Conference on Machine Learning, pp. 486–499. PMLR (2022)
Baena-Garcıa, M., del Campo-Ávila, J., Fidalgo, R., Bifet, A.: Early drift detection method. In: In 4th International Workshop on Knowledge Discovery from Data Streams. Citeseer (2006)
Balzanella, A., Verde, R.: Histogram-based clustering of multiple data streams. Knowl. Inf. Syst. 62(1), 203–238 (2020)
Bifet, A., Gavalda, R.: Learning from time-changing data with adaptive windowing. In: Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 443–448. SIAM (2007)
Chandra, S., Haque, A., Khan, L., Aggarwal, C.: An adaptive framework for multistream classification. In: Proceedings of the 25th ACM International Conference on Information and Knowledge Management, pp. 1181–1190. Indianapolis, IN, USA (2016)
Elwell, R., Polikar, R.: Incremental learning of concept drift in nonstationary environments. IEEE Trans. Neural Netw. 22(10), 1517–1531 (2011)
Fang, Z., Lu, J., Liu, F., Xuan, J., Zhang, G.: Open set domain adaptation: theoretical bound and algorithm. IEEE Trans. Neural Netw. Learn. Syst. 32(10), 4309–4322 (2020)
Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with drift detection. In: Bazzan, A.L.C., Labidi, S. (eds.) Advances in Artificial Intelligence – SBIA 2004. SBIA 2004. LNCS, vol. 3171, pp. 286–295. Springer, Berlin, Heidelberg (2004). https://doi.org/10.1007/978-3-540-28645-5_29
Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., Bouchachia, A.: A survey on concept drift adaptation. ACM Comput. Surv. 46(4), 44 (2014)
Haque, A., Wang, Z., Chandra, S., Dong, B., Khan, L., Hamlen, K.W.: Fusion: an online method for multistream classification. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 919–928. Singapore (2017)
Jaysawal, B.P., Huang, J.W.: PSP-AMS: progressive mining of sequential patterns across multiple streams. ACM Trans. Knowl. Discov. Data 13(1), 1–23 (2018)
Kolter, J.Z., Maloof, M.A.: Dynamic weighted majority: an ensemble method for drifting concepts. J. Mach. Learn. Res. 8, 2755–2790 (2007)
Krawczyk, B., Minku, L.L., Gama, J., Stefanowski, J., Woźniak, M.: Ensemble learning for data stream analysis: a survey. Inf. Fusion 37, 132–156 (2017)
Li, W., Yang, X., Liu, W., Xia, Y., Bian, J.: DDG-DA: data distribution generation for predictable concept drift adaptation. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 4092–4100 (2022)
Liu, A., Lu, J., Liu, F., Zhang, G.: Accumulating regional density dissimilarity for concept drift detection in data streams. Pattern Recogn. 76, 256–272 (2018)
Liu, A., Song, Y., Zhang, G., Lu, J.: Regional concept drift detection and density synchronized drift adaptation. In: IJCAI International Joint Conference on Artificial Intelligence (2017)
Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., Zhang, G.: Learning under concept drift: a review. IEEE Trans. Knowl. Data Eng. 31(12), 2346–2363 (2018)
Lu, J., Niu, L., Zhang, G.: A situation retrieval model for cognitive decision support in digital business ecosystems. IEEE Trans. Ind. Electron. 60(3), 1059–1069 (2012)
Lu, J., Yang, X., Zhang, G.: Support vector machine-based multi-source multi-attribute information integration for situation assessment. Expert Syst. Appl. 34(2), 1333–1340 (2008)
Lu, J., Zuo, H., Zhang, G.: Fuzzy multiple-source transfer learning. IEEE Trans. Fuzzy Syst. 28(12), 3418–3431 (2019)
Mihai, N., Alexandru, M., Bala-Constantin, Z.: Multimodal emotion detection from multiple data streams for improved decision making. Procedia Comput. Sci. 214, 1082–1089 (2022)
Nadeem, A., Verwer, S.: SECLEDS: sequence clustering in evolving data streams via multiple medoids and medoid voting. In: Amini, M.R., Canu, S., Fischer, A., Guns, T., Kralj Novak, P., Tsoumakas, G. (eds.) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. LNCS, vol. 13713, pp. 157–173. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-26387-3_10
Otero, A., Félix, P., Márquez, D.G., García, C.A., Caffarena, G.: A fault-tolerant clustering algorithm for processing data from multiple streams. Inf. Sci. 584, 649–664 (2022)
Sangma, J.W., Pal, V., Kumar, N., Kushwaha, R., et al.: FHC-NDS: fuzzy hierarchical clustering of multiple nominal data streams. IEEE Trans. Fuzzy Syst. 31(3), 786–798 (2022)
Song, Y., Zhang, G., Lu, H., Lu, J.: A fuzzy drift correlation matrix for multiple data stream regression. In: 2020 IEEE International Conference on Fuzzy Systems, pp. 1–6. IEEE (2020)
Yu, E., Song, Y., Zhang, G., Lu, J.: Learn-to-adapt: concept drift adaptation for hybrid multiple streams. Neurocomputing 496, 121–130 (2022)
Yu, H., Liu, W., Lu, J., Wen, Y., Luo, X., Zhang, G.: Detecting group concept drift from multiple data streams. Pattern Recogn. 134, 109113 (2023)
Zhou, M., Song, Y., Zhang, G., Zhang, B., Lu, J.: An efficient Bayesian neural network for multiple data streams. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2021)
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This work was supported by the Australian Research Council under Grants DP200100700 and FL190100149.
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Wang, K., Lu, J., Liu, A., Zhang, G. (2024). An Augmented Learning Approach for Multiple Data Streams Under Concept Drift. In: Liu, T., Webb, G., Yue, L., Wang, D. (eds) AI 2023: Advances in Artificial Intelligence. AI 2023. Lecture Notes in Computer Science(), vol 14471. Springer, Singapore. https://doi.org/10.1007/978-981-99-8388-9_32
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DOI: https://doi.org/10.1007/978-981-99-8388-9_32
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