Abstract
Deep stochastic configuration network (DSCN) is an incremental learning method for large-scale data analysis and processing, which has the advantages of lower human intervention, higher learning efficiency and stronger generalization ability. For improving the stability of DSCN, a deep stochastic configuration network based on AdaBoost is proposed, termed as AdaBoost-DSCN. In our proposed model, the AdaBoost learning approach is adopted, the weights of the base models are adjusted adaptively according to the training results, then the base models are combined to generate a stronger model, which is beneficial to reduce the influence of random parameters on network performance of DSCN. Experimental results on complex function approximation problems and large-scale regression datasets show that AdaBoost-DSCN has higher regression accuracy for large-scale data regression analysis compared with DSCN, AdaBoost-SCN, SCN.
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
Wang, D., Li, M.: Stochastic configuration networks: fundamentals and algorithms. IEEE Trans. Cybern. 47(10), 3466–3479 (2017)
Huang, C., Huang, Q., Wang, D.: Stochastic configuration networks based adaptive storage replica management for power big data processing. IEEE Trans. Indust. Inf. 16(1), 373–383 (2020)
Pang, L., Guo, L., Zhang, J.: Subject-specific mental workload classification using EEG and stochastic configuration network (SCN). Biomed. Sign. Process. Contr. 68, 102711 (2021)
Liu, J., Hao, R., Zhang, T.: Vibration fault diagnosis based on stochastic configuration neural networks. Neurocomputing 434, 98–125 (2021)
Wang, W., Wang, D.: Prediction of component concentrations in sodium aluminate liquor using stochastic configuration networks. Neural Comput. Appl. 32(17), 13625–13638 (2020)
Wang, W., Jia, Y., Yu, W.: On-line ammonia nitrogen measurement using generalized additive model and stochastic configuration networks. Measurement 170, 108743 (2021)
Pan, J., Luan, F., Gao, Y.: FPGA-based implementation of stochastic configuration network for robotic grasping recognition. IEEE Access 8, 139966–139973 (2020)
Zhao, J., Hu, T., Zheng, R.: Defect recognition in concrete ultrasonic detection based on wavelet packet transform and stochastic configuration networks. IEEE Access 9, 9284–9295 (2021)
Li, M., Wang, D.: 2-D stochastic configuration networks for image data analytics. IEEE Trans. Cybern. 51(1), 359–372 (2021)
Wang, D., Li, M.: Deep stochastic configuration networks with universal approximation property. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE, Rio de Janeiro Brazil (2018)
Wang, D., Cui, C.: Stochastic configuration networks ensemble with heterogeneous features for large-scale data analytics. Inf. Sci. 417, 55–71 (2017)
Ai, W., Wang, D.: Distributed stochastic configuration networks with cooperative learning paradigm. Inf. Sci. 540, 1–16 (2020)
Zhang, C., Ding, S., Zhang, J.: Parallel stochastic configuration networks for large-scale data regression. Appl. Soft Comput. 103, 107143 (2021)
Huang, C., Li, M., Wang, D.: Stochastic configuration network ensembles with selective base models. Neural Netw. 137, 106–118 (2021)
Pratama, M., Wang, D.: Deep stacked stochastic configuration networks for lifelong learning of non-stationary data streams. Inf. Sci. 495, 150–174 (2019)
Li, K., Wang, W., Wang, Y.: Application of ensemble stochastic configuration network in aquaculture water quality monitoring. Trans. Chin. Soc. Agricult. Eng. (Trans. CSAE) 36(4), 220–226 (2020)
Qu, H., Feng, T., Wang, Y.: AdaBoost SCN algorithm for optics fiber vibration signal recognition. Appl. Opt. 58(21), 5612–5623 (2019)
Lu, J., Ding, J.: Construction of prediction intervals for carbon residual of crude oil based on deep stochastic configuration networks. Inf. Sci. 486, 119–132 (2019)
Hansen, L.K., Salamon, P.: Neural network ensembles. IEEE Trans. Pattern Anal. Mach. Intell. 12(10), 993–1001 (1990)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of online learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)
Opitz, D.W., Maclin, R.F.: An empirical evaluation of bagging and boosting for artificial neural networks. In: Proceedings of International Conference on Neural Networks (ICNN 1997), pp. 1401–1405. IEEE, Houston (1997)
Liu, L., Hua, Y., Zhao, Q.: Blind image quality assessment by relative gradient statistics and AdaBoosting neural network. Signal Process. Image Commun. 40, 1–15 (2016)
Luo, Y., Wang, B.: Prediction of negative conversion days of childhood nephrotic syndrome based on PCA and BP-AdaBoost neural network. IEEE Access 7, 151579–151586 (2019)
Li, S., Wang, J., Liu, B.: Prediction of market demand based on AdaBoost_BP neural network. In: 2013 International Conference on Computer Sciences and Applications, pp. 305–308. IEEE, Wuhan (2013)
Tyukin, I. Y., Prokhorov, D. V.: Feasibility of random basis function approximators for modeling and control. In: 2009 IEEE Control Applications, (CCA) & Intelligent Control, (ISIC), pp. 1391–1396. IEEE, St. Petersburg (2009)
Li, M., Wang, D.: Insights into randomized algorithms for neural networks: practical issues and common pitfalls. Inf. Sci. 382, 170–178 (2017)
Acknowledgements
This work is supported by the National Natural Science Foundation of China under Grant No. 61976216 and No. 61672522.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 IFIP International Federation for Information Processing
About this paper
Cite this paper
Zhang, C., Ding, S., Ding, L. (2022). An AdaBoost Based - Deep Stochastic Configuration Network. In: Shi, Z., Zucker, JD., An, B. (eds) Intelligent Information Processing XI. IIP 2022. IFIP Advances in Information and Communication Technology, vol 643. Springer, Cham. https://doi.org/10.1007/978-3-031-03948-5_1
Download citation
DOI: https://doi.org/10.1007/978-3-031-03948-5_1
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-03947-8
Online ISBN: 978-3-031-03948-5
eBook Packages: Computer ScienceComputer Science (R0)
