Abstract
With the development of information technology, more and more data are generated from social life. Concept drift detection in multi-class classification system has gradually become a research hotspot in the field of data mining. To solve this problem, a multi-class concept drift detection algorithm based on Restricted Boltzmann Machine is proposed in this paper. Based on the probability distribution of RBM, the KL divergence and concept drift detection coefficients are constructed to detect concept drift and judge its type. The performance of the algorithm is tested and analyzed on simulation and real data sets.
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
B Zhang G Chen X Wang 2010 Fuzzy clustering based on data flow model Comput. Eng. Appl. 46 124 126
J Schlimmer RH Granger Jr 1986 Incremental learning from noisy data Mach. Learn. 1 317 354
C Zhou 2011 Detection and classification of concept drift on data stream Minicomputer Sys. 32 421 425
G Tsoumakas I Katakis 2009 Multi-label classification: an overview Int. J. Data Warehous. Min. 3 1 13
N Li 2013 Concept Drift Data Stream Classification Algorithm and its Application Fujian Normal University Fuzhou
Y Sun 2008 Mining concept drift in data stream based on multiple classifiers J. Automation 34 93 97
M Liu 2014 Online Concept Drift Detection Based on Data Window **angtan University **angtan
Y Zhang Y Chai L Wang 2013 Martingale based concept drift detection method for data stream Minicomputer Syst. 34 1787 1792
P Li X Wu X Hu Q Liang Y Gao 2010 A random decision tree ensemble for mining concept drifts from noisy data streams Appl. Artif. Intell. 24 680 710
Y Sun K Tang LL Minku S Wang X Yao 2016 Online ensemble learning of data streams with gradually evolved classes IEEE Trans. Knowl. Data Eng. 28 1532 1545
J Lu A Liu F Dong F Gu J Gama G Zhang 2018 Learning under concept drift: a review IEEE Trans. Knowl. Data Eng. 31 2346 2363
Acknowledgements
The work was supported by the Natural Science Foundation of China (61731006, 61971310)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhou, J., Zhu, Q., Shi, R., Wang, W. (2022). Concept Drift Detection Based on Restricted Boltzmann Machine in Multi-class Classification System. In: Liang, Q., Wang, W., Mu, J., Liu, X., Na, Z. (eds) Artificial Intelligence in China. Lecture Notes in Electrical Engineering, vol 854. Springer, Singapore. https://doi.org/10.1007/978-981-16-9423-3_29
Download citation
DOI: https://doi.org/10.1007/978-981-16-9423-3_29
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-9422-6
Online ISBN: 978-981-16-9423-3
eBook Packages: Computer ScienceComputer Science (R0)