Dealing with small sample size problems in process industry using virtual sample generation: a Kriging-based approach-Reference-Cited by-同舟云学术

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Dealing with small sample size problems in process industry using virtual sample generation: a Kriging-based approach

Published:2019-09-25 Issue:9 Volume:24 Page:6889-6902
ISSN:1432-7643
Container-title:Soft Computing
language:en
Short-container-title:Soft Comput

Author:

Zhu Qun-Xiong,Chen Zhong-Sheng^ORCID,Zhang Xiao-Han,Rajabifard Abbas,Xu Yuan,Chen Yi-Qun

Funder

National Natural Science Foundation of China

the Fundamental Research Funds for the Central Universities

the China Scholarship Council State-Sponsored Scholarship Program

the Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, WUHAN University

Publisher

Springer Science and Business Media LLC

Subject

Geometry and Topology,Theoretical Computer Science,Software

Link

http://link.springer.com/content/pdf/10.1007/s00500-019-04326-3.pdf

Reference30 articles.

1. Bouhlel MA, Martins JRRA (2018) Gradient-enhanced Kriging for high-dimensional problems. Eng Comput 35:157–173

2. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357

3. Chen Z-S, Zhu B, He Y-L, Yu L-A (2017) A PSO based virtual sample generation method for small sample sets: applications to regression datasets. Eng Appl Artif Intell 59:236–243

4. Dong Y, Zhang Z, Hong W-C (2018) A hybrid seasonal mechanism with a chaotic cuckoo search algorithm with a support vector regression model for electric load forecasting. Energies 11:1009

5. Feng S, Zhou H, Dong H (2019) Using deep neural network with small dataset to predict material defects. Mater Des 162:300–310

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