Online Machine Learning from Non-stationary Data Streams in the Presence of Concept Drift and Class Imbalance: A Systematic Review
DOI:
https://doi.org/10.32890/jict2024.23.1.5Keywords:
Concept Adaptation, Concept Drift, Class Imbalance, Data Streams, Non-stationaryAbstract
In IoT environment applications generate continuous non-stationary data streams with in-built problems of concept drift and class imbalance which cause classifier performance degradation. The imbalanced data affects the classifier during concept detection and concept adaptation. In general, for concept detection, a separate mechanism is added in parallel with the classifier to detect the concept drift called a drift detector. For concept adaptation, the classifier updates itself or trains a new classifier to replace the older one. In case, the data stream faces a class imbalance issue, the classifier may not properly adapt to the latest concept. In this survey, we study how the existing work addresses the issues of class imbalance and concept drift while learning from nonstationary
data streams. We further highlight the limitation of existing work and challenges caused by other factors of class imbalance along
with concept drift in data stream classification. Results of our survey found that, out of 1110 studies, by using our inclusion and exclusion criteria, we were able to narrow the pool of articles down to 35 that directly addressed our study objectives. The study found that issues such as multiple concept drift types, dynamic class imbalance ratio, and multi-class imbalance in presence of concept drift are still open for further research. We also observed that, while major research efforts have been dedicated to resolving concept drift and class imbalance, not much attention has been given to with-in-class imbalance, rear examples, and borderline instances when they exist with concept drift in multi-class data. This paper concludes with some suggested future directions.
References
Abbasi, M., Shahraki, A., & Taherkordi, A. (2021). Deep learning for network traffic monitoring and analysis (NTMA): A survey. Computer Communications, 170, 19-41. https://doi.org/10.1016/j.comcom.2021.01.021 Journal of ICT, 23, No. 1 (January) 2024, pp: 105-
Agrahari, S., & Singh, A. K. (2021). Concept Drift Detection in Data Stream Mining: A literature review. Journal of King Saud University - Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2021.11.006
Amin, A., Al-Obeidat, F., Shah, B., Adnan, A., Loo, J., & Anwar, S. (2019). Customer churn prediction in telecommunication industry using data certainty. Journal of Business Research, 94, 290-301.
Ancy, S., & Paulraj, D. (2020). Handling imbalanced data with concept drift by applying dynamic sampling and ensemble classification model. Computer Communications, 153, 553-560. https://doi.org/10.1016/j.comcom.2020.01.061
Arabmakki, E., Kantardzic, M., & Sethi, T. S. (2017, 14-19 May 2017). A partial labeling framework for multi-class imbalanced streaming data. Paper presented at the 2017 International Joint Conference on Neural Networks (IJCNN).
Aydogdu, O., & Ekinci, M. (2020). A new approach for data stream classification: Unsupervised feature representational online sequential extreme learning machine. Multimedia Tools and Applications, 79(37-38), 27205-27227. https://doi.org/10.1007/ s11042-020-09300-y
Bahri, M., Bifet, A., Gama, J., Gomes, H. M., & Maniu, S. (2021). Data stream analysis: Foundations, major tasks and tools. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 11(3), e1405. https://doi.org/10.1002/widm.1405
Bernardo, A., & Della Valle, E. (2021). VFC-SMOTE: Very fast continuous synthetic minority oversampling for evolving data streams. Data Mining and Knowledge Discovery, 35(6), 2679-2713. https://doi.org/10.1007/s10618-021-00786-0
Bifet, A., & Gavalda, R. (2007). Learning from time-changing data with adaptive windowing. Paper presented at the Proceedings of the 2007 SIAM international conference on data mining.
Branco, P., Torgo, L., & Ribeiro, R. P. (2016). A survey of predictive modeling on imbalanced domains. ACM Computing Surveys (CSUR), 49(2), 1-50.
Brzezinski, D., Minku, L. L., Pewinski, T., Stefanowski, J., & Szumaczuk, A. (2021). The impact of data difficulty factors on classification of imbalanced and concept drifting data streams. Knowledge and Information Systems, 63(6), 1429-1469. https://doi.org/10.1007/s10115-021-01560-w Journal of ICT, 23, No. 1 (January) 2024, pp: 105-
Cabral, G. G., & Minku, L. L. (2022). Towards Reliable Online Just-in-time Software Defect Prediction. IEEE Transactions on Software Engineering, 1-1. 10.1109/TSE.2022.3175789
Cano, A., & Krawczyk, B. (2022). ROSE: Robust online self-adjusting ensemble for continual learning on imbalanced drifting data streams. Machine Learning, 111(7), 2561-2599. https://doi.org/10.1007/s10994-022-06168-x
Chowdhury, A., Raut, S., & Pal, A. (2022). Internet of Things resource monitoring through proactive fault prediction. Computers and Industrial Engineering, 169. https://doi.org/10.1016/j. cie.2022.108265
Dal Pozzolo, A., Boracchi, G., Caelen, O., Alippi, C., & Bontempi, G. (2018). Credit card fraud detection: A realistic modeling and a novel learning strategy. IEEE transactions on neural networks and learning systems, 29(8), 3784-3797. https://doi.org/10.1109/TNNLS.2017.2736643
Fauzi, M. F. A. M., Aziz, I. A., & Amiruddin, A. (2019). The Prediction of Remaining Useful Life (RUL) in Oil and Gas Industry using Artificial Neural Network (ANN) Algorithm. Paper presented at the 2019 IEEE Conference on Big Data and Analytics (ICBDA).
Fazal, K., & Alotaibi, S. (2020). Applications of artificial intelligence and big data analytics in m-health: A healthcare system perspective. Journal of Healthcare Engineering, 2020, https://doi.org/10.1155/2020/8894694
Gama, J., Zliobaite, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A. (2014). A survey on concept drift adaptation. ACM Computing Surveys, 46(4). https://doi.org/10.1145/2523813
Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A. (2014). A survey on concept drift adaptation. ACM Computing Surveys (CSUR), 46(4), 1-37.
Ghazikhani, A., Monsefi, R., & Yazdi, H. S. (2013). Ensemble of online neural networks for non-stationary and imbalanced data streams. Neurocomputing, 122, 535-544.
Han, M., Chen, Z., Li, M., Wu, H., & Zhang, X. (2022). A survey of active and passive concept drift handling methods. Computational Intelligence. 38(4), 1492-1535. https://doi.org/10.1111/coin.12520
Heusinger, M., Raab, C., & Schleif, F.-M. (2022). Passive concept drift handling via variations of learning vector quantisation. Neural Computing and Applications, 34(1), 89-100. https://doi.org/10.1007/s00521-020-05242-6 Journal of ICT, 23, No. 1 (January) 2024, pp: 105-
Hu, Z. X., & Jiang, P. (2019). An Imbalance Modified Deep Neural Network With Dynamical Incremental Learning for Chemical Fault Diagnosis. IEEE Transactions on Industrial Electronics, 66(1), 540-550. https://doi.org/10.1109/tie.2018.2798633
Jameel, S. M., Hashmani, M. A., Alhussain, H., Rehman, M., & Budiman, A. (2020). A critical review on adverse effects of concept drift over machine learning classification models. International Journal of Advanced Computer Science and Applications (IJACSA), 11(1).
Jani, R., Bhatt, N., & Shah, C. (2019). Handling concept drift in data stream classification. International Journal of Innovative Technology and Exploring Engineering, 8(10), 548-550. https://doi.org/10.35940/ijitee.J8857.0881019
Jayasinghe, L., Samarasinghe, T., Yuen, C., Low, J. C. N., & Ge, S. S. (2018). Temporal convolutional memory networks for remaining useful life estimation of industrial machinery. In 2019 IEEE International Conference on Industrial Technology (ICIT) (pp. 915-920).
Jiao, B., Guo, Y., Gong, D., & Chen, Q. (2022). Dynamic ensemble selection for imbalanced data streams with concept drift. IEEE transactions on neural networks and learning systems, 1-14. https://doi.org/10.1109/TNNLS.2022.3183120
Karbasi, S. (2020). Fast and accurate concept drift detection from event logs. Journal of Information and Communication Technology, 43(43), 105.
Kaur, H., Pannu, H. S., & Malhi, A. K. (2019). A systematic review on imbalanced data challenges in machine learning: Applications and solutions. ACM Computing Surveys, 52(4). https://doi.org/10.1145/3343440
Keele, S. (2007). Guidelines for performing systematic literature reviews in software engineering. Retrieved from https://www.researchgate.net/profile/Barbara-Kitchenham/ publication/302924724_Guidelines_for_performing_ Systematic_Literature_Reviews_in_Software_Engineering/ l i n k s / 6 1 7 1 2 9 3 2 7 6 6 c 4 a 2 11 c 0 3 a 6 f 7 / G u i d e l i n e s - f o r-performing-Systematic-Literature-Reviews-in-Software-Engineering.pdf
Khandekar, V. S., & Shrinath, P. (2022). Hybrid dynamic chunk ensemble model for multi-class data streams. Indonesian Journal of Electrical Engineering and Computer Science, 25(2), 1115-1122. https://doi.org/10.11591/ijeecs.v25.i2.pp1115-1122 Journal of ICT, 23, No. 1 (January) 2024, pp: 105-
Khandekar, V. S., & Srinath, P. (2020). Non-stationary Data Stream Analysis: State-of-the-Art Challenges and Solutions. In Proceeding of International Conference on Computational Science and Applications: ICCSA 2019 (pp. 67-80). Springer Singapore.
Klikowski, J., & Woźniak, M. (2022). Deterministic Sampling Classifier with weighted Bagging for drifted imbalanced data stream classification. Applied Soft Computing, 122. https://doi.org/10.1016/j.asoc.2022.108855
Kolajo, T., Daramola, O., & Adebiyi, A. (2019). Big data stream analysis: A systematic literature review. Journal of Big Data, 6(1), 47.
Korycki, L., & Krawczyk, B. (2021). Concept drift detection from multi-class imbalanced data streams. In 2021 IEEE 37th International Conference on Data Engineering (ICDE) (pp. 1068-1079). IEEE.
Krawczyk, B., & Cano, A. (2018). Online ensemble learning with abstaining classifiers for drifting and noisy data streams. Applied Soft Computing, 68, 677-692.
Kulkarni, R., Revathy, S., & Patil, S. (2021). A Novel Approach to Maximise G-mean in Nonstationary Data with Recurrent Imbalance Shifts. International Arab Journal of Information Technology, 18(1), 103-113. https://doi.org/10.34028/ iajit/18/1/12
Kulkarni, R. V., Revathy, S., & Patil, S. H. (2022). Smart pools of data with ensembles for adaptive learning in dynamic data streams with class imbalance. IAES International Journal of Artificial Intelligence, 11(1), 310-318. https://doi.org/10.11591/ijai.v11. i1.pp310-318
Kumar, R. L., Kakarla, J., Isunuri, B. V., & Singh, M. (2021). Multi-class brain tumor classification using residual network and global average pooling. Multimedia Tools and Applications, 80, 13429-13438.
Li, Q. D., Xiong, Q. Y., Ji, S. F., Yu, Y., Wu, C., & Gao, M. (2021). Incremental semi-supervised Extreme Learning Machine for Mixed data stream classification. Expert Systems with Applications, 185. https://doi.org/10.1016/j.eswa.2021.115591
Li, X., Ding, Q., & Sun, J.-Q. (2018). Remaining useful life estimation in prognostics using deep convolution neural networks. Reliability Engineering & System Safety, 172, 1-11. Journal of ICT, 23, No. 1 (January) 2024, pp: 105-
Li, Z., Huang, W. C., Xiong, Y., Ren, S. Q., & Zhu, T. F. (2020). Incremental learning imbalanced data streams with concept drift: The dynamic updated ensemble algorithm. Knowledge-Based Systems, 195. https://doi.org/10.1016/j.knosys.2020.105694
Lin, C.-C., Deng, D.-J., Kuo, C.-H., & Chen, L. (2019). Concept drift detection and adaption in big imbalance industrial IoT data using an ensemble learning method of offline classifiers. IEEE Access, 7, 56198-56207.
Liu, W., Zhang, H., Ding, Z., Liu, Q., & Zhu, C. (2021). A comprehensive active learning method for multiclass imbalanced data streams with concept drift. Knowledge-Based Systems, 215, 106778. https://doi.org/10.1016/j.knosys.2021.106778
Liu, W. K., Zhang, H., Ding, Z. Y., Liu, Q. B., & Zhu, C. (2021). A comprehensive active learning method for multiclass imbalanced data streams with concept drift. Knowledge-Based Systems, 215. https://doi.org/10.1016/j.knosys.2021.106778
Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., & Zhang, G. (2019). Learning under Concept Drift: A Review. IEEE Transactions on Knowledge and Data Engineering, 31(12), 2346-2363. https://doi.org/10.1109/TKDE.2018.2876857
Lu, Y., Cheung, Y. M., & Yan Tang, Y. (2020). Adaptive chunk-based dynamic weighted majority for imbalanced data streams with concept drift. IEEE transactions on neural networks and learning systems, 31(8), 2764-2778. https://doi.org/10.1109/ TNNLS.2019.2951814
Malialis, K., Panayiotou, C. G., & Polycarpou, M. M. (2021). Online Learning with Adaptive Rebalancing in Nonstationary Environments. IEEE transactions on neural networks and learning systems, 32(10), 4445-4459. https://doi.org/10.1109/ TNNLS.2020.3017863
Mansour, R. F., Al-Otaibi, S., Al-Rasheed, A., Aljuaid, H., Pustokhina, I. V., & Pustokhin, D. A. (2021). An optimal big data analytics with concept drift detection on high-dimensional streaming data. Computers, Materials and Continua, 68(3), 2843-2858. https://doi.org/10.32604/cmc.2021.016626
Margara, A., & Rabl, T. (2019). Definition of Data Streams. In S. Sakr & A. Y. Zomaya (Eds.), Encyclopedia of Big Data Technologies (pp. 648-652). Cham: Springer International Publishing.
Meseguer, J., Puig, V., & Escobet, T. (2010). Fault diagnosis using a timed discrete-event approach based on interval observers: Application to sewer networks. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 40(5), 900-916. Journal of ICT, 23, No. 1 (January) 2024, pp: 105-
Metz, D. (2018). Tackling urban traffic congestion: The experience of London, Stockholm and Singapore. Case Studies on Transport Policy, 6(4), 494-498.
Palli, A.-S., Jaafar, J., Hashmani, M.-A., Gomes, H.-M., Alsughayyir, A., & Gilal, A.-R. (2023). Combined Effect of Concept Drift and Class Imbalance on Model Performance During Stream Classification, 75(1), 1827-1845.
Palli, A. S., Jaafar, J., Gomes, H. M., Hashmani, M. A., & Gilal, A. R. (2022). An Experimental Analysis of Drift Detection Methods on Multi-Class Imbalanced Data Streams. Applied Sciences, 12(22), 11688. https://doi.org/10.3390/app122211688
Palli, A. S., Jaafar, J., & Hashmani, M. A. (2021). Performance Degradation of Multi-class Classification Model Due to Continuous Evolving Data Streams. In: Vol. 72. Lecture Notes on Data Engineering and Communications Technologies (pp. 696-706): Springer Science and Business Media Deutschland GmbH.
Palli, A. S., Jaafar, J., Hashmani, M. A., Gomes, H. M., & Gilal, A. R. (2022). A Hybrid Sampling Approach for Imbalanced Binary and Multi-Class Data Using Clustering Analysis. IEEE Access, 10, 118639-118653. https://doi.org/10.1109/ ACCESS.2022.3218463
Pongpaichet, S., Jankapor, S., Janchai, S., & Tongsanit, T. (2020). Early detection at-risk students using machine learning. In 2020 International Conference on Information and Communication Technology Convergence (ICTC) (pp. 283-287). IEEE.
Prasad, K. S. N., Rao, A. S., & Ramana, A. V. (2020). Ensemble framework for concept-drift detection in multidimensional streaming data. International Journal of Computers and Applications, 44(12), 1193-1200. https://doi.org/10.1080/1206 212X.2020.1711617
Priya, S., & Uthra, A. (2022). Adaptive Synthetic Oversampling Algorithm for Handling Class Imbalance in Multi-Class Data Stream Classification. Journal of Computer Science, 18(7), 650-664. https://doi.org/10.3844/jcssp.2022.650.664
Ren, S., Liao, B., Zhu, W., Li, Z., Liu, W., & Li, K. (2018). The Gradual Resampling Ensemble for mining imbalanced data streams with concept drift. Neurocomputing, 286, 150-166. https://doi.org/10.1016/j.neucom.2018.01.063
Ren, S. Q., Zhu, W., Liao, B., Li, Z., Wang, P., Li, K. Q.,...
Li, Z. J. (2019). Selection-based resampling ensemble algorithm for nonstationary imbalanced stream data learning. Knowledge-Based Systems, 163, 705-722. https://doi.org/10.1016/j. knosys.2018.09.032 Journal of ICT, 23, No. 1 (January) 2024, pp: 105-
Roseberry, M., Krawczyk, B., & Cano, A. (2019). Multi-label punitive kNN with self-adjusting memory for drifting data streams. ACM Transactions on Knowledge Discovery from Data, 13(6). https://doi.org/10.1145/3363573
Sainin, M. S., Alfred, R., & Ahmad, F. (2021). Ensemble meta classifier with sampling and feature selection for data with imbalance multiclass problem. Journal of Information and Communication Technology, 20(2), 103-133.
Soares, R. G. F., & Minku, L. L. (2023). OSNN: An Online Semisupervised Neural Network for Nonstationary Data Streams. IEEE transactions on neural networks and learning systems, 34(9), 6029-6041. https://doi.org/10.1109/ TNNLS.2021.3132584
Spelmen, V. S., & Porkodi, R. (2018). A review on handling imbalanced data. In 2018 international conference on current trends towards converging technologies (ICCTCT) (pp. 1-11). IEEE.
Sun, Y., Sun, Y., & Dai, H. (2020). Two-stage cost-sensitive learning for data streams with concept drift and class imbalance. IEEE Access, 8, 191942-191955. https://doi.org/10.1109/ ACCESS.2020.3031603
Sun, Y. G., Li, M., Li, L., Shao, H., & Sun, Y. (2021). Cost-Sensitive Classification for Evolving Data Streams with Concept Drift and Class Imbalance. Comput Intell Neurosci, 2021. https://doi.org/10.1155/2021/8813806
Taha, A. Y., Tiun, S., Abd Rahman, A. H., & Sabah, A. (2021). Multilabel over-sampling and under-sampling with class alignment for imbalanced multilabel text classification. Journal of Information and Communication Technology, 20(3), 423-456.
Toor, A. A., & Usman, M. (2022). Adaptive telecom churn prediction for concept-sensitive imbalance data streams. Journal of Supercomputing, 78(3), 3746-3774. https://doi.org/10.1007/ s11227-021-04021-x
Toor, A. A., Usman, M., Younas, F., Fong, A. C. M., Khan, S. A., & Fong, S. (2020). Mining Massive E-Health Data Streams for IoMT Enabled Healthcare Systems. Sensors (Basel), 20(7). https://doi.org/10.3390/s20072131
Toor, A. A., Usman, M., Younas, F., Habib, M., & Fong, A. C. M. (2020). Efficient mining of IoT based data streams for advanced computer vision systems. Multimedia Tools and Applications, 1-16. https://doi.org/10.1007/s11042-020-09175-z Journal of ICT, 23, No. 1 (January) 2024, pp: 105-
Vafaie, P., Viktor, H., & Michalowski, W. (2020). Multi-class imbalanced semi-supervised learning from streams through online ensembles. In 2020 International Conference on Data Mining Workshops (ICDMW) (pp. 867-874). IEEE
Vasantha Kokilam, K., Ponmary Pushpa Latha, D., & Joseph Pushpa Raj, D. (2019). Learning of concept drift and multi class imbalanced dataset using resampling ensemble methods. International Journal of Recent Technology and Engineering, 8(1), 1332-1340.
Wang, T., Zhao, S., Zhu, G., & Zheng, H. J. A. e. (2021). A machine learning-based early warning system for systemic banking crises, 53(26), 2974-2992.
Zhang, G., Yang, D., Galanis, G., & Androulakis. (2022). Solar forecasting with hourly updated numerical weather prediction. Renewable and Sustainable Energy Reviews, 154, 111768.
Zhang, H., & Liu, Q. B. (2019). Online Learning Method for Drift and Imbalance Problem in Client Credit Assessment. Symmetry, 11(7), 890. https://doi.org/10.3390/sym11070890
Zhang, H., Liu, W., & Liu, Q. (2022). Reinforcement Online Active Learning Ensemble for Drifting Imbalanced Data Streams. IEEE Transactions on Knowledge and Data Engineering, 34(8), 3971-3983. https://doi.org/10.1109/TKDE.2020.3026196
Zhang, H., Liu, W., Shan, J., & Liu, Q. (2018). Online Active Learning Paired Ensemble for Concept Drift and Class Imbalance. IEEE Access, 6, 73815-73828. https://doi.org/10.1109/ ACCESS.2018.2882872
Zhang, H., Liu, W., Wang, S., Shan, J., & Liu, Q. (2019). Resample-based ensemble framework for drifting imbalanced data streams. IEEE Access, 7, 65103-65115. https://doi.org/10.1109/ ACCESS.2019.2914725
Zhong, X., & Enke, D. (2019). Predicting the daily return direction of the stock market using hybrid machine learning algorithms. Financial Innovation, 5(1), 1-20.
Zyblewski, P., Sabourin, R., & Woźniak, M. (2021). Preprocessed dynamic classifier ensemble selection for highly imbalanced drifted data streams. Information Fusion, 66, 138-154. https://doi.org/10.1016/j.inffus.2020.09.004
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