Semi-supervised active learning for support vector machines: A novel approach that exploits structure information in data

Calma, Adrian; Reitmaier, Tobias; Sick, Bernhard

doi:10.1016/j.ins.2018.04.063

Cited by 33 publications

(14 citation statements)

References 17 publications

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“…This is because, in some practical cases, it is difficult to obtain sufficient fault data and labels. Moreover, active learning [295][296][297][298] and transfer learning [230,299] which can address the issues of real-life fault detection and diagnosis cases using unlabeled data should be seriously considered. However, the negative transfer should be avoided in engineering scenarios.…”

Section: Discussionmentioning

confidence: 99%

Review on Fault Detection and Diagnosis Feature Engineering in Building Heating, Ventilation, Air Conditioning and Refrigeration Systems

Liu

et al. 2021

IEEE Access

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Section: Discussionmentioning

confidence: 99%

Review on Fault Detection and Diagnosis Feature Engineering in Building Heating, Ventilation, Air Conditioning and Refrigeration Systems

Liu

et al. 2021

IEEE Access

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“…For this reason, Zhou et al [16] proposed a safe semi-supervised support vector machine (S4VM). Bernhard Sick et al [17] showed that a semi-supervised support vector machine (SemiSVM) can well exploit structure information in data and greatly improve identification performance with unlabeled data. On the other hand, the large number of instruments in the actual chemical process brings noise and redundant process variables.…”

Section: Background and Significancementioning

confidence: 99%

“…The process includes 22 continuous measured variables (Table 2) and 20 preset fault modes (Table 3). 3E Feed CMV (14) Separator underflow CMV 4A and C Feed CMV (15) Stripper level CMV (5) Recycle flow CMV (16) Stripper pressure CMV (6) Reactor feed CMV (17) Stripper underflow CMV 7Reactor pressure CMV (18) Stripper temperature CMV (8) Reactor level CMV (19) Stripper steam flow 5Recycle flow CMV (16) Stripper pressure CMV (6) Reactor feed CMV (17) Stripper underflow CMV 7Reactor pressure CMV (18) Stripper temperature CMV (8) Reactor level CMV (19) Stripper steam flow CMV (9) Reactor temperature CMV (20) Compressor work CMV (10) Purge flow CMV (21) Reactor cooling water outlet temperature CMV (11) Separator temperature CMV (22) Condenser cooling water outlet temperature Figure 4 shows that the total variance contribution rate of the first 12 principal components has reached 83.15% (more than 80%), so the first 12 principal components can reflect information about all variables. Figure 5 shows the eigenvalues of the first 12 principal components as well.…”

Section: Process Descriptionmentioning

confidence: 99%

A Dynamic Active Safe Semi-Supervised Learning Framework for Fault Identification in Labeled Expensive Chemical Processes

Jia

Tian

et al. 2020

Processes

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A novel active semi-supervised learning framework using unlabeled data is proposed for fault identification in labeled expensive chemical processes. A principal component analysis (PCA) feature selection strategy is first given to calculate the weight of the variables. Secondly, the identification model is trained based on the obtained key process variables. Thirdly, the pseudo label confidence of identification model is dynamically optimized with an historical, current, and future pseudo label confidence mean. To increase the upper limit of the identification model that is self-learning with high entropy process data, active learning is used to identify process data and diagnosis fault causes by ontology. Finally, a PCA-dynamic active safe semi-supervised support vector machine (PCA-DAS4VM) for fault identification in labeled expensive chemical processes is built. The application in the Tennessee Eastman (TE) process shows that this hybrid technology is able to: (i) eliminate chemical process noise and redundant process variables simultaneously, (ii) combine historical pseudo label confidence with future pseudo label confidence to improve the identification accuracy of abnormal working conditions, (iii) efficiently select and diagnose high entropy unlabeled process data, and (iv) fully utilize unlabeled data to enhance the identification performance.

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“…Generally, multiple semi-supervised terms lead to major empirical improvements on real-world tasks [21], [22]. SALSVM [23] and SELSVM [24] realize an active learning and ensemble learning technique for SVM, respectively. Unfortunately, these offline semi-supervised learning algorithms are still unable to address long-playing large-scale OS 2 L problems directly because of the constraint of time and memory.…”

Section: Introductionmentioning

confidence: 99%

Online Semi-Supervised Learning With Multiple Regularization Terms

Chen

Sun

et al. 2019

IEEE Access

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Online semi-supervised learning (OS 2 L) has received much attention recently because of its well practical usefulness. Most of the existing studies of OS 2 L are related to manifold regularization. In this paper, we introduce a novel OS 2 L framework with multiple regularization terms based on the notion of ascending the dual function in constrained optimization. Using the Fenchel conjugate, different semisupervised regularization terms can be integrated into the dual function easily and directly. This approach is derived by updating limited dual coefficient variables on each learning round. To be practical, we also employ buffering strategies and sparse approximation approaches in this paper. The experimental studies show that our methods achieve accuracy comparable to offline algorithms while consuming less time and memory. Especially, our OS 2 L algorithms can handle the settings where the target hyperplane of classification continually drifts with the sequence of arriving instances. This paper paves a way to design and analyze OS 2 L algorithms with multiple regularization terms. INDEX TERMSOnline semi-supervised learning (OS 2 L), SVM, manifold regularization, co-regularization, Fenchel conjugate.

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Semi-supervised active learning for support vector machines: A novel approach that exploits structure information in data

Cited by 33 publications

References 17 publications

Review on Fault Detection and Diagnosis Feature Engineering in Building Heating, Ventilation, Air Conditioning and Refrigeration Systems

Review on Fault Detection and Diagnosis Feature Engineering in Building Heating, Ventilation, Air Conditioning and Refrigeration Systems

A Dynamic Active Safe Semi-Supervised Learning Framework for Fault Identification in Labeled Expensive Chemical Processes

Online Semi-Supervised Learning With Multiple Regularization Terms

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