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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.

First Page

452

Revised Date

2020-11-13

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.

Corresponding Author

Weili Xiong,greenpre@163.com

DOI

10.16182/j.issn1004731x.joss.20-0788

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