Abstract
In order to address the interference of concept drift on the results of multi-label learning algorithms, a hybrid kernel extreme learning machine is used as the foundation for the classification algorithm. Concept drift detection is incorporated, and the classifier is updated based on the detection results for application in multi-label learning. Firstly, the data stream is divided into appropriately sized data blocks, and a hybrid extreme learning machine is used on several of the preceding data blocks to obtain the base classifier. Subsequently, the incoming data blocks are processed using the base classifier to calculate the sample mean and variance between the current data and previous data. Based on this result, it is determined whether concept drift has occurred, and the base classifiers within the ensemble model are retrained and adjusted to update the model.
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
References
Halstead B, Koh YS, Riddle P, Pechenizkiy M, Bifet A (2023) Combining diverse meta-features to accurately identify recurring concept drift in data streams. ACM Trans Knowl Discov Data 17(8):1–36
Han M, Chen Z, Li M, Wu H, Zhang X (2022) A survey of active and passive concept drift handling methods. Comput Intell 38(4):1492–1535
Qian K, Min XY, Cheng Y, Min F (2023) Weight matrix sharing for multi-label learning. Pattern Recogn 136:109156
Zhang ML, Zhou ZH (2007) ML-KNN: a lazy learning approach to multi-label learning. Pattern Recogn 40(7):2038–2048
Xu X, Shan D, Li S, Sun T, Xiao P, Fan J (2019) Multi-label learning method based on ML-RBF and laplacian ELM. Neurocomputing 331:213–219
Alzaqebah A, Aljarah I, Al-Kadi O (2023) A hierarchical intrusion detection system based on extreme learning machine and nature-inspired optimization. Comput Secur 124:102957
Luo F, Guo W, Yu Y, Chen G (2017) A multi-label classification algorithm based on kernel extreme learning machine. Neurocomputing 260:313–320
Wang YB, Pei GS, Cheng YS (2019) Multi-label learning algorithm of an elastic net kernel extreme learning machine. CAAI Trans Intell Syst 04:831–842
Qian M, Tang JK (2019) Multi-label learning based on mixed kernel extreme learning machine. J Chongqing Univ Sci Technol (Nat Sci Edn) 02:79–85
Ren SQ (2018) Research on ensemble classification algorithms of data stream based onconcept drift. Hunan University, PhD dissertation
Zhang N, Ding S, Zhang J (2016) Multi-layer ELM-RBF for multi-label learning. Appl Soft Comput 43:535–545
Acknowledgements
This work was supported in part by Natural Research Science Institute of Anhui Provincial Department of Education 2022AH051379; Suzhou University School Level Quality Engineering Project szxy2023jyjf82; Suzhou University School Level Scientific research platform open project 2022ykf03; Suzhou University School Level Innovative and Entrepreneurial Teaching and Learning Research Project szxy2022ccjy04.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Tang, J., Zhou, W., Sun, H. (2024). Application Research of Multi-label Learning Under Concept Drift. In: Wang, W., Liu, X., Na, Z., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2023. Lecture Notes in Electrical Engineering, vol 1033. Springer, Singapore. https://doi.org/10.1007/978-981-99-7502-0_44
Download citation
DOI: https://doi.org/10.1007/978-981-99-7502-0_44
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-7555-6
Online ISBN: 978-981-99-7502-0
eBook Packages: EngineeringEngineering (R0)