典型文献
Ensemble enhanced active learning mixture discriminant analysis model and its application for semi-supervised fault classification
文献摘要：
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,we introduce several reasonable indexes and criteria,and thus human labeling interference is greatly reduced.Finally,the fault classification effectiveness of the proposed method is evaluated using a numerical example and the Tennessee Eastman process.
文献关键词：
中图分类号：
作者姓名：
Weijun WANG;Yun WANG;Jun WANG;Xinyun FANG;Yuchen HE
作者机构：
Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province,China Jiliang University,Hangzhou 310018,China;Mechanical and Electrical Engineering Department,Zhejiang Tongji Vocational College of Science and Technology,Hangzhou 311231,China;Suzhou Institute of Metrology,Suzhou 215004,China
文献出处：
引用格式：
[1]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].信息与电子工程前沿（英文）,2022(12):1814-1827
A类：
B类：
Ensemble,enhanced,active,learning,mixture,discriminant,analysis,model,its,application,semi,supervised,fault,classification,indispensable,part,process,monitoring,performance,relies,heavily,sufficiency,knowledge,However,labels,are,always,difficult,acquire,because,limited,sampling,condition,expensive,laboratory,which,may,lead,deterioration,To,handle,this,dilemma,new,strategy,performed,employed,each,unlabeled,respect,specific,dataset,Unlabeled,samples,large,values,will,serve,supplementary,information,training,In,addition,introduce,several,reasonable,indexes,criteria,thus,human,labeling,interference,greatly,reduced,Finally,effectiveness,proposed,method,evaluated,using,numerical,example,Tennessee,Eastman
AB值：
0.673667
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