Abstract
Online machine learning plays a pivotal role in the 4th Industrial Revolution (IR 4.0). IR 4.0 requires real-time data analysis (classification or prediction) using the streaming data. However, mostly the data streams contain nonstationary (variability features) characteristics, such as concept drift and class imbalance. The issues of concept drift and class imbalance adversely affects the accuracy of the classification models. The classification accuracy is even more affected when these issues arrived at the same time (joint problem). Some efforts have been made in the literature to cope with the joint problem of class imbalance and concept drift in online learning, but the existing solutions are limited to binary class classification and these solutions do not work for multi-class classification. Besides, the literature doesn’t mention the exact correlation between the critical factors of concept drift and class imbalance. Also, the tuning parameters of the multi-class classification models, which can help in improving the classification accuracy, are unknown. However, to resolve the joint problem of concept drift and class imbalance in online multi-class classification models, it is essential to determine the exact correlation and find the tuning parameters, which could be helpful to provide a more dynamic approach for avoiding the performance degradation of the multi-class classification models. Therefore, to resolve this issue, this study aims to determine the correlation between the concept drift and class imbalance, identify the tuning parameters of multi-class classification models and propose a dynamic solution based on these findings. Our proposed dynamic approach could be effectively utilized in various online machine learning based (real-time) streams analysis, which is desirable for IR 4.0.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., Gao, R.X.: Deep learning and its applications to machine health monitoring. Mech. Syst. Signal Process. 115, 213–237 (2019)
Mazurowski, M.A., Habas, P.A., Zurada, J.M., Lo, J.Y., Baker, J.A., Tourassi, G.D.: Training neural network classifiers for medical decision making: the effects of imbalanced datasets on classification performance. Neural Netw. 21(2–3), 427–436 (2008)
Zhang, J., Zulkernine, M.: Network intrusion detection using random forests. In: Book Network Intrusion Detection using Random Forests. Citeseer (2005)
Wang, G.: Asymmetric random subspace method for imbalanced credit risk evaluation. In: Software Engineering and Knowledge Engineering: Theory and Practice, pp. 1047–1053. Springer, Heidelberg (2012)
Jameel, S.M.H., Rehman, M.A., Budiman, M.: An adaptive deep learning framework for dynamic image classification in the Internet of Things. Sensors 20, 5811 (2020)
Hashmani, M.A., Jameel, S.M., Al-Hussain, H., Rehman, M., Budiman, A.: Accuracy performance degradation in image classification models due to concept drift. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 10(5), 1–5 (2019)
Jameel, S.M., Hashmani, M.A., Alhussain, H., Rehman, M., Budiman, A.: ‘A Critical Review on Adverse Effects of Concept Drift over Machine Learning Classification Models’
Farid, D.M., Zhang, L., Hossain, A., Rahman, C.M., Strachan, R., Sexton, G., Dahal, K.: An adaptive ensemble classifier for mining concept drifting data streams. Expert Sys Appl 40(15), 5895–5906 (2013)
Bifet, A., Gavalda, R.: Learning from time-changing data with adaptive windowing. In: Book Learning from Time-Changing Data with Adaptive Windowing, pp. 443–448. SIAM (2007)
Jameel, S.M., Hashmani, M.A., Rehman, M., Budiman, A.: Adaptive CNN ensemble for complex multispectral image analysis. Complexity 2020 (2020)
Jameel, S.M., Gilal, A.R., Rizvi, S.S.H., Rehman, M., Hashmani, M.A.: Practical implications and challenges of multispectral image analysis. In: Book Practical Implications and Challenges of Multispectral Image Analysis, pp. 1–5. IEEE (2020)
Mirza, B., Lin, Z., Toh, K.-A.: Weighted online sequential extreme learning machine for class imbalance learning. Neural Process. Lett. 38(3), 465–486 (2013)
Mirza, B., Lin, Z., Cao, J., Lai, X.: Voting based weighted online sequential extreme learning machine for imbalance multi-class classification. In: Book Voting Based Weighted Online Sequential Extreme Learning Machine for Imbalance Multi-class Classification, pp. 565–568. IEEE (2015)
Mustaqeem, A., Anwar, S.M., Majid, M.: Multiclass classification of cardiac arrhythmia using improved feature selection and SVM invariants. Computat. Math. Methods Med. 2018, 1–10 (2018)
Priya, S., Uthra, R.A.: Comprehensive analysis for class imbalance data with concept drift using ensemble based classification. J. Ambient Intell. Humanized Comput. (2020)
Iwashita, A.S., Papa, J.P.: An overview on concept drift learning. IEEE Access 7, 1532–1547 (2018)
Wang, S., Minku, L.L., Yao, X.: A systematic study of online class imbalance learning with concept drift. IEEE Trans. Neural Netw. Learn. Syst. 29(10), 4802–4821 (2018)
Ancy, S., Paulraj, D.: Handling imbalanced data with concept drift by applying dynamic sampling and ensemble classification model. Comput. Commun. 153, 553–560 (2020)
Khandekar, V.S., Srinath, P.: ‘Non-stationary data stream analysis: state-of-the-art challenges and solutions. In: Book Non-stationary Data Stream Analysis: State-of-the-Art Challenges and Solutions, pp. 67–80. Springer, Heidelberg (2020)
Zheng, X., Li, P., Chu, Z., Xuegang, H.: A survey on multi-label data stream classification. IEEE Access 8, 1249–1275 (2020)
Wang, S., Minku, L.L., Yao, X.: Dealing with multiple classes in online class imbalance learning. In: Book Dealing with Multiple Classes in Online Class Imbalance Learning, pp. 2118–2124 (2016)
Chen, S., He, H.: SERA: selectively recursive approach towards nonstationary imbalanced stream data mining. In: Book SERA: Selectively Recursive Approach Towards Nonstationary Imbalanced Stream Data Mining, pp. 522–529 (2009)
Chen, S., He, H.: Towards incremental learning of nonstationary imbalanced data stream: a multiple selectively recursive approach. Evol. Syst. 2(1), 35–50 (2011)
Wang, S., Minku, L.L., Yao, X.: A learning framework for online class imbalance learning. In: Book A Learning Framework for Online Class Imbalance learning, pp. 36–45. IEEE (2013)
Wang, S., Minku, L.L., Yao, X.: Resampling-based ensemble methods for online class imbalance learning. IEEE Trans. Knowl. Data Eng. 27(5), 1356–1368 (2015)
Ghazikhani, A., Monsefi, R., Yazdi, H.S.: Ensemble of online neural networks for non-stationary and imbalanced data streams. Neurocomputing 122, 535–544 (2013)
Mirza, B., Lin, Z., Liu, N.: Ensemble of subset online sequential extreme learning machine for class imbalance and concept drift. Neurocomputing 149, 316–329 (2015)
Wang, L., Wu, C.: Dynamic imbalanced business credit evaluation based on Learn++ with sliding time window and weight sampling and FCM with multiple kernels. Inf Sci 520, 305–323 (2020)
Sun, Y., Tang, K., Minku, L.L., Wang, S., Yao, X.: Online ensemble learning of data streams with gradually evolved classes. IEEE Trans. Knowl. Data Eng. 28(6), 1532–1545 (2016)
Li, Z., Huang, W., Xiong, Y., Ren, S., Zhu, T.: Incremental learning imbalanced data streams with concept drift: the dynamic updated ensemble algorithm. Knowl.-Based Syst. 195, 105694 (2020)
Lin, C.-C., Deng, D.-J., Kuo, C.-H., Chen, L.: Concept drift detection and adaption in big imbalance industrial IoT data using an ensemble learning method of offline classifiers. IEEE Access 7, 56198–56207 (2019)
Wang, H., Abraham, Z.: Concept drift detection for streaming data. In: Book Concept Drift Detection for Streaming Data, pp. 1–9. IEEE (2015)
Kokilam, K.V., Latha, D.P.P., Raj, D.J.P.: Learning of concept drift and multi class imbalanced dataset using resampling ensemble methods. Int. J. Recent Technol. Eng. 8(1), 1332–1340 (2019)
Nayer Wanas, A.F., Said, D., Khodeir, N., Fayek, M.: Detection and handling of different types of concept drift in news recommendation systemS. Int. J. Comput. Sci. Inf. Technol. (IJCSIT) 11, 87–106 (2019)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Palli, A.S., Jaafar, J., Hashmani, M.A. (2021). Performance Degradation of Multi-class Classification Model Due to Continuous Evolving Data Streams. In: Saeed, F., Mohammed, F., Al-Nahari, A. (eds) Innovative Systems for Intelligent Health Informatics. IRICT 2020. Lecture Notes on Data Engineering and Communications Technologies, vol 72. Springer, Cham. https://doi.org/10.1007/978-3-030-70713-2_63
Download citation
DOI: https://doi.org/10.1007/978-3-030-70713-2_63
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-70712-5
Online ISBN: 978-3-030-70713-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)