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Journal of Zhejiang University SCIENCE C 1998 Vol.-1 No.-1 P.

http://doi.org/10.1631/FITEE.2200053


Ensemble enhanced active learning mixture discriminant analysis model and its application for semi-supervised fault classification


Author(s):  Weijun WANG, Yun WANG, Jun WANG, Xinyun FANG, Yuchen HE

Affiliation(s):  Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province; more

Corresponding email(s):   S20010811027@cjlu.edu.cn, wy@zjtongji.edu.cn, blackknight@cjlu.edu.cn, yche@cjlu.edu.cn

Key Words:  Semi-supervised, Active learning, Ensemble learning, Mixture discriminant analysis, Fault classification


Weijun WANG, Yun WANG, Jun WANG, Xinyun FANG, Yuchen HE. Ensemble enhanced active learning mixture discriminant analysis model and its application for semi-supervised fault classification[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .

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author="Weijun WANG, Yun WANG, Jun WANG, Xinyun FANG, Yuchen HE",
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year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2200053"
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%J Journal of Zhejiang University SCIENCE C
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Abstract: 
As an indispensable part of process monitoring, the performance of fault classification relies heavily on the sufficiency of process knowledge. However, data labels are always difficult to acquire because of the limited sampling condition or expensive laboratory analysis, which may lead to deterioration of classification performance. To handle this dilemma, a new semi-supervised fault classification strategy is performed in which enhanced active learning is employed to evaluate the value of each unlabeled sample with respect to a specific labeled dataset. Unlabeled samples with large values will serve as supplementary information for the training dataset. In addition, human labeling interference is greatly reduced because several reasonable indexes and criteria are introduced. Finally, the fault classification effectiveness of the proposed method is evaluated using a numerical example and the Tennessee Eastman process (TEP).

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