A Survey on Incremental Learning Techniques for Streaming data
DOI:
https://doi.org/10.29304/jqcsm.2026.18.12522Keywords:
Machine Learning, Supervised Learning, Unsupervised Learning, Incremental Learning, Streaming Data, Concept DriftAbstract
The rapid growth of data stream applications, such as Internet of Things (IoT) systems, smart environments, and real-time analytics, has intensified the need for learning models capable of adapting to continuously evolving data distributions. The traditional techniques of batch learning presuppose fixed data and as such they find it hard to sustain performance when concept drift occurs where the characteristics of data evolve slowly, suddenly or repeatedly with time. Incremental learning has become one of the most important solutions to this issue because it allows models to keep on updating themselves with new incoming data and retain the information previously learned and does not force them to retrain at a high cost. This survey provides an in-depth overview of incremental learning approaches that are used in the streaming data setting with concept drift. We critically review supervised, unsupervised and semi-supervised machine learning methods and deep learning and hybrid methods. The review studies the fundamental adaptation techniques, such as sliding windows, replay, prototype-based learning, methods of detecting drift, expansion of dynamic architecture, and stability-plasticity balancing. To each category we comment on the underlying mechanisms, strength, weakness, computational efficiency and their ability to adapt to various drift conditions. Overall, 38 more recent studies are critically analyzed and compared in the wide range of application areas, data sets, and indicators of assessment. Some of the challenges that are highlighted in this survey include catastrophic forgetting, scalability, interpretability, and resistance to complex and recurring concept drift. Lastly, we recognize the important research gaps and establish future plans on how to develop coherent, scalable and explainable incremental learning models of actual streaming data systems in the world.
Downloads
References
B. Halstead, Y. S. Koh, P. Riddle, M. Pechenizkiy, and A. Bifet, “A Probabilistic Framework for Adapting to Changing and Recurring Concepts in Data Streams,” in 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA), Oct. 2022, pp. 1–10. doi: 10.1109/DSAA54385.2022.10032368.
G. M. van de Ven, N. Soures, and D. Kudithipudi, “Continual Learning and Catastrophic Forgetting,” 2025, pp. 153–168. doi: 10.1016/B978-0-443-15754-7.00073-0.
G. Shi, J. Chen, W. Zhang, L.-M. Zhan, and X.-M. Wu, “Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat Minima”.
J. Gama, I. Žliobaitė, A. Bifet, M. Pechenizkiy, and A. Bouchachia, “A survey on concept drift adaptation,” ACM Comput. Surv., vol. 46, no. 4, p. 44:1-44:37, Mar. 2014, doi: 10.1145/2523813.
L. Wang, X. Zhang, H. Su, and J. Zhu, “A Comprehensive Survey of Continual Learning: Theory, Method and Application,” Feb. 06, 2024, arXiv: arXiv:2302.00487. doi: 10.48550/arXiv.2302.00487.
N. Zucchet, R. Meier, S. Schug, A. Mujika, and J. Sacramento, “Online learning of long-range dependencies,” Advances in Neural Information Processing Systems, vol. 36, pp. 10477–10493, Dec. 2023.
I. C. Martin, S. Mukherjee, A. Baimagambetov, J. Vanschoren, and N. Polatidis, “Evolving Machine Learning: A Survey,” Oct. 10, 2025, arXiv: arXiv:2505.17902. doi: 10.48550/arXiv.2505.17902.
Z. Li and D. Hoiem, “Learning without Forgetting,” Feb. 14, 2017, arXiv: arXiv:1606.09282. doi: 10.48550/arXiv.1606.09282.
T. Wu, L. Luo, Y.-F. Li, S. Pan, T.-T. Vu, and G. Haffari, “Continual Learning for Large Language Models: A Survey,” Feb. 07, 2024, arXiv: arXiv:2402.01364. doi: 10.48550/arXiv.2402.01364.
L. Boukela, G. Zhang, M. Yacoub, and S. Bouzefrane, “A near-autonomous and incremental intrusion detection system through active learning of known and unknown attacks,” in 2021 International Conference on Security, Pattern Analysis, and Cybernetics（SPAC), Jun. 2021, pp. 374–379. doi: 10.1109/SPAC53836.2021.9539947.
Y. Ouyang, J. Shi, H. Wei, and H. Gao, “Incremental Learning for Personalized Recommender Systems,” Aug. 13, 2021, arXiv: arXiv:2108.13299. doi: 10.48550/arXiv.2108.13299.
C. Li, Y. Tang, and G. Xu, “SVMWPE-HLLE Based Fault Diagnosis Approach for Rolling Bearing,” IEEE Access, vol. 11, pp. 4645–4659, 2023, doi: 10.1109/ACCESS.2023.3234528.
D. Mulimani, P. Patil, S. Totad, and R. Benni, “Online Detection and Adaptation of Concept Drift in Streaming Data Classification,” Procedia Computer Science, vol. 235, pp. 2803–2811, 2024, doi: 10.1016/j.procs.2024.04.265.
T. Wang, X. Li, and Y. Wang, “SAR Image Simulations of Ocean Scenes Based on the Improved Facet TSM,” Sensors, vol. 23, no. 5, p. 2564, Feb. 2023, doi: 10.3390/s23052564.
Z. Tao, S. Huang, and G. Wang, “Prototypes Sampling Mechanism for Class Incremental Learning,” IEEE Access, vol. 11, pp. 81942–81952, 2023, doi: 10.1109/ACCESS.2023.3301123.
H. Gâlmeanu and R. Andonie, “Concept Drift Adaptation with Incremental–Decremental SVM,” Applied Sciences, vol. 11, no. 20, p. 9644, Oct. 2021, doi: 10.3390/app11209644.
J. Yoon, E. Yang, J. Lee, and S. J. Hwang, “Lifelong Learning with Dynamically Expandable Networks,” Jun. 11, 2018, arXiv: arXiv:1708.01547. doi: 10.48550/arXiv.1708.01547.
F. Giannini, G. Ziffer, and E. Della Valle, “cPNN: Continuous Progressive Neural Networks for Evolving Streaming Time Series,” in Advances in Knowledge Discovery and Data Mining, vol. 13938, H. Kashima, T. Ide, and W.-C. Peng, Eds., in Lecture Notes in Computer Science, vol. 13938. , Cham: Springer Nature Switzerland, 2023, pp. 328–340. doi: 10.1007/978-3-031-33383-5_26.
G. Lin, H. Chu, and H. Lai, “Towards Better Plasticity-Stability Trade-off in Incremental Learning: A Simple Linear Connector,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA: IEEE, Jun. 2022, pp. 89–98. doi: 10.1109/CVPR52688.2022.00019.
J. Gama, I. Žliobaitė, A. Bifet, M. Pechenizkiy, and A. Bouchachia, “A survey on concept drift adaptation,” ACM Comput. Surv., vol. 46, no. 4, pp. 1–37, Apr. 2014, doi: 10.1145/2523813.
H. Gao, Z. Ding, and M. Pan, “Incremental Learning Method for Data with Delayed Labels,” cai, vol. 41, no. 5, pp. 1260–1283, 2022, doi: 10.31577/cai_2022_5_1260.
R. Ramanath et al., “Lambda Learner: Fast Incremental Learning on Data Streams,” in Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, Virtual Event Singapore: ACM, Aug. 2021, pp. 3492–3502. doi: 10.1145/3447548.3467172.
Ž. Stržinar, I. Škrjanc, M. Pratama, and B. Pregelj, “Evolving Clustering of Time Series for Unsupervised Analysis of Industrial Data Streams,” 2024, SSRN. doi: 10.2139/ssrn.5026151.
Ž. Stržinar, I. Škrjanc, and B. Pregelj, “Evolving interval-based time series clustering for streaming industrial data,” Evolving Systems, vol. 16, no. 3, p. 82, Sep. 2025, doi: 10.1007/s12530-025-09713-w.
B. Krawczyk, “Tensor decision trees for continual learning from drifting data streams,” Mach Learn, vol. 110, no. 11–12, pp. 3015–3035, Dec. 2021, doi: 10.1007/s10994-021-06054-y.
E. Lopez-Lopez, X. M. Pardo, and C. V. Regueiro, “Incremental Learning from Low-labelled Stream Data in Open-Set Video Face Recognition,” Pattern Recognition, vol. 131, p. 108885, Nov. 2022, doi: 10.1016/j.patcog.2022.108885.
H. Gâlmeanu and R. Andonie, “Concept Drift Adaptation with Incremental–Decremental SVM,” Applied Sciences, vol. 11, no. 20, p. 9644, Oct. 2021, doi: 10.3390/app11209644.
A. Cano and B. Krawczyk, “ROSE: robust online self-adjusting ensemble for continual learning on imbalanced drifting data streams,” Mach Learn, vol. 111, no. 7, pp. 2561–2599, Jul. 2022, doi: 10.1007/s10994-022-06168-x.
D. Jayaratne, D. De Silva, D. Alahakoon, and X. Yu, “Continuous detection of concept drift in industrial cyber-physical systems using closed loop incremental machine learning,” Discov Artif Intell, vol. 1, no. 1, p. 7, Dec. 2021, doi: 10.1007/s44163-021-00007-z.
F. Periti, S. Montanelli, A. Ferrara, and S. Castano, “Incremental Affinity Propagation Based on Cluster Consolidation and Stratification,” Neural Process Lett, vol. 57, no. 3, p. 44, Apr. 2025, doi: 10.1007/s11063-025-11752-y.
C. W. Chiu and L. L. Minku, “SMOClust: Synthetic Minority Oversampling based on Stream Clustering for Evolving Data Streams,” Aug. 28, 2023, arXiv: arXiv:2308.14845. doi: 10.48550/arXiv.2308.14845.
A. Chaudhari, P. Mulay, A. Agarwal, K. Iyer, and S. Sarbhai, “DIC2FBA: Distributed Incremental Clustering with Closeness Factor Based Algorithm for Analysis of Smart Meter Data,” IJCDS, vol. 15, no. 1, pp. 29–38, Jul. 2024, doi: 10.12785/ijcds/160103.
A. Bhanderi and R. Bhatnagar, “Incremental Gaussian Mixture Clustering for Data Streams,” in 2024 IEEE International Conference on Data Mining Workshops (ICDMW), Dec. 2024, pp. 197–204. doi: 10.1109/ICDMW65004.2024.00032.
A. Zubaroğlu and V. Atalay, “Online embedding and clustering of evolving data streams,” Statistical Analysis, vol. 16, no. 1, pp. 29–44, Feb. 2023, doi: 10.1002/sam.11590.
J. Chen, J. Chen, and Z. Zhong, “A weight clustering algorithm based on sliding window model for stream data,” Sci Rep, vol. 15, no. 1, p. 22521, Jul. 2025, doi: 10.1038/s41598-025-96696-y.
A. Al-Shammari, “A Fully Online Clustering Approach for Enhanced Performance of Health Information System,” J. Al-Qadisiyah Comp. Sci. Math., vol. 15, no. 2, Sep. 2023, doi: 10.29304/jqcm.2023.15.2.1239.
“An Incremental Clustering Algorithm for Arbitrary Shaped in IoT Data,” AJETS, vol. 7, no. 3, 2024, doi: 10.25236/AJETS.2024.070325.
C. Mu, Y. Hou, J. Zhao, S. Wei, and Y. Wu, “Stream-DBSCAN: A Streaming Distributed Clustering Model for Water Quality Monitoring,” Applied Sciences, vol. 13, no. 9, p. 5408, Apr. 2023, doi: 10.3390/app13095408.
Q. Zeng, H. Bao, Y. Hu, Z. Zhang, Y. Zheng, and L. Wen, “TNStream: Applying Tightest Neighbors to Micro-Clusters to Define Multi-Density Clusters in Streaming Data,” May 01, 2025, arXiv: arXiv:2505.00359. doi: 10.48550/arXiv.2505.00359.
X. Bi, C. Zhang, X. Zhao, D. Li, Y. Sun, and Y. Ma, “CODES: Efficient Incremental Semi-Supervised Classification Over Drifting and Evolving Social Streams,” IEEE Access, vol. 8, pp. 14024–14035, 2020, doi: 10.1109/ACCESS.2020.2965766.
L. Alzubaidi et al., “A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications,” J Big Data, vol. 10, no. 1, p. 46, Apr. 2023, doi: 10.1186/s40537-023-00727-2.
M. A. Shyaa, Z. Zainol, R. Abdullah, M. Anbar, L. Alzubaidi, and J. Santamaría, “Enhanced Intrusion Detection with Data Stream Classification and Concept Drift Guided by the Incremental Learning Genetic Programming Combiner,” Sensors, vol. 23, no. 7, p. 3736, Apr. 2023, doi: 10.3390/s23073736.
C. D. Kim, J. Jeong, S. Moon, and G. Kim, “Continual Learning on Noisy Data Streams via Self-Purified Replay,” in 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada: IEEE, Oct. 2021, pp. 517–527. doi: 10.1109/ICCV48922.2021.00058.
E. Sarmas, S. Strompolas, V. Marinakis, F. Santori, M. A. Bucarelli, and H. Doukas, “An Incremental Learning Framework for Photovoltaic Production and Load Forecasting in Energy Microgrids,” Electronics, vol. 11, no. 23, p. 3962, Nov. 2022, doi: 10.3390/electronics11233962.
C. Wang, G. Tan, S. B. Roy, and B. C. Ooi, “Distribution-aware Online Continual Learning for Urban Spatio-Temporal Forecasting,” Nov. 24, 2024, arXiv: arXiv:2411.15893. doi: 10.48550/arXiv.2411.15893.
Y. Liu, B. Schiele, and Q. Sun, “Adaptive Aggregation Networks for Class-Incremental Learning,” in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA: IEEE, Jun. 2021, pp. 2544–2553. doi: 10.1109/CVPR46437.2021.00257.
Ł. Korycki and B. Krawczyk, “Concept Drift Detection from Multi-Class Imbalanced Data Streams,” Apr. 20, 2021, arXiv: arXiv:2104.10228. doi: 10.48550/arXiv.2104.10228.
S.-H. Kim, C. Lee, and C.-H. Youn, “An Accelerated Edge Cloud System for Energy Data Stream Processing Based on Adaptive Incremental Deep Learning Scheme,” IEEE Access, vol. 8, pp. 195341–195358, 2020, doi: 10.1109/ACCESS.2020.3033771.
M. Rostami and A. Galstyan, “Cognitively Inspired Learning of Incremental Drifting Concepts,” Apr. 21, 2023, arXiv: arXiv:2110.04662. doi: 10.48550/arXiv.2110.04662.
B. S. Prashanth et al., “Adaptive Buffering Strategies for Incremental Learning Under Concept Drift in Lifestyle Disease Modeling,” IEEE Access, vol. 13, pp. 174001–174033, 2025, doi: 10.1109/ACCESS.2025.3616776.
D. Leite, A. Silva, G. Casalino, A. Sharma, D. Fortunato, and A.-C. Ngomo, “EGNN-C+: Interpretable Evolving Granular Neural Network and Application in Classification of Weakly-Supervised EEG Data Streams,” Feb. 26, 2024, arXiv: arXiv:2402.17792. doi: 10.48550/arXiv.2402.17792.
D.-W. Zhou, Y. Yang, and D.-C. Zhan, “Learning to Classify With Incremental New Class,” IEEE Trans. Neural Netw. Learning Syst., vol. 33, no. 6, pp. 2429–2443, Jun. 2022, doi: 10.1109/TNNLS.2021.3104882.
A. Eisa, N. EL-Rashidy, M. Dahman Alshehri, H. M. El-bakry, and S. Abdelrazek, “Incremental Learning Framework for Mining Big Data Stream,” Computers, Materials & Continua, vol. 71, no. 2, pp. 2901–2921, 2022, doi: 10.32604/cmc.2022.021342.
E. Yu, J. Lu, and G. Zhang, “Generalized Incremental Learning under Concept Drift across Evolving Data Streams,” Jun. 06, 2025, arXiv: arXiv:2506.05736. doi: 10.48550/arXiv.2506.05736.
X. Zhao, Z. Wang, L. Gao, Y. Li, and S. Wang, “Incremental face clustering with optimal summary learning via graph convolutional network,” Tinshhua Sci. Technol., vol. 26, no. 4, pp. 536–547, Aug. 2021, doi: 10.26599/TST.2020.9010024.
Q. Zhou, Z.-Y. Wang, and L. Huang, “ELM-KL-LSTM: a robust and general incremental learning method for efficient classification of time series data,” PeerJ Computer Science, vol. 9, p. e1732, Dec. 2023, doi: 10.7717/peerj-cs.1732.
C. Lamers, R. Vidal, N. Belbachir, N. Van Stein, T. Bäck, and P. Giampouras, “Clustering-based Domain-Incremental Learning,” in 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Paris, France: IEEE, Oct. 2023, pp. 3376–3384. doi: 10.1109/ICCVW60793.2023.00363.
R. Hu, Luc, Chen, and Y. Wang, “An Efficient Outlier Detection Algorithm for Data Streaming,” Jan. 02, 2025, arXiv: arXiv:2501.01061. doi: 10.48550/arXiv.2501.01061.
A. Ashfahani and M. Pratama, “Unsupervised Continual Learning in Streaming Environments,” Sep. 20, 2021, arXiv: arXiv:2109.09282. doi: 10.48550/arXiv.2109.09282.
C. Liu et al., “On the Adaptation to Concept Drift for CTR Prediction,” Feb. 23, 2023, arXiv: arXiv:2204.05101. doi: 10.48550/arXiv.2204.05101.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Akram Asdkhan Mussa, Ali Saeed Alfoudi, Ali Hakem Asaeedi
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
