Journal of System Simulation
Abstract
Abstract: Aiming at lack of tag samples and high cost of sampling tags in complex industrial processes, an active learning algorithm based on probability selection is proposed. Firstly, unlabeled samples are performed subspace integration by using the principal component analysis. Then, the information of unlabeled samples is evaluated by the uncertainty, which is calculated based on the out put of all sub learners. And the most valuable samples are selected to mark manually. Finally, the function of unlabeled samples and labeled samples are analyzed, and the termination conditions are designed by introducing the performance index of training set. Through simulations of industrial processes data, it is verified that the proposed method can improve the accuracy of the model while reducing the cost of marking.
Recommended Citation
Dai, Xuezhi and Xiong, Weili
(2021)
"Active Learning Intelligent Soft Sensor based on Probability Selection,"
Journal of System Simulation: Vol. 33:
Iss.
6, Article 14.
DOI: 10.16182/j.issn1004731x.joss.20-0093
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol33/iss6/14
First Page
1350
Revised Date
2020-04-29
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.20-0093
Last Page
1357
CLC
TP274
Recommended Citation
Dai Xuezhi, Xiong Weili. Active Learning Intelligent Soft Sensor based on Probability Selection[J]. Journal of System Simulation, 2021, 33(6): 1350-1357.
DOI
10.16182/j.issn1004731x.joss.20-0093
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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