Journal of System Simulation
Abstract
Abstract: Since the data collected from industrial processes often contain a large number of unlabeled samples, while the number of labeled samples is small and the cost of manual labeling is high, an active learning method based on covariance matrix is proposed. This method uses labeled samples to establish a Gaussian process regression model, and constructs the covariance matrix between the unlabeled samples, using the value of the determinant of the covariance matrix as an evaluation indicator. While selecting informative unlabeled samples, the similarity between samples is measured to avoid redundant addition of samples, which finally improves model prediction accuracy at the same labeling cost. The application simulation of the algorithm based on industrial process data verifies the feasibility and effectiveness of the proposed method.
Recommended Citation
Zhou, Bowen and Xiong, Weili
(2022)
"Research on Active Learning Method and Application Based on Covariance Matrix,"
Journal of System Simulation: Vol. 34:
Iss.
3, Article 3.
DOI: 10.16182/j.issn1004731x.joss.20-0788
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol34/iss3/3
First Page
452
Revised Date
2020-11-13
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.20-0788
Last Page
460
CLC
TP274
Recommended Citation
Bowen Zhou, Weili Xiong. Research on Active Learning Method and Application Based on Covariance Matrix[J]. Journal of System Simulation, 2022, 34(3): 452-460.
DOI
10.16182/j.issn1004731x.joss.20-0788
Included in
Artificial Intelligence and Robotics Commons, Computer Engineering Commons, Numerical Analysis and Scientific Computing Commons, Operations Research, Systems Engineering and Industrial Engineering Commons, Systems Science Commons