Active learning based support vector data description method for robust novelty detection

Yin, Lili; Wang, Huangang; Fan, Wenhui

doi:10.1016/j.knosys.2018.04.020

Cited by 45 publications

(17 citation statements)

References 15 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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“…The basic distance-based methods are LOF [29] and its modification [30]. (3) Boundarybased approach, mainly involving OCSVM [31] and SVDD [32], typically try to define a boundary around the normal class data. Whether the unknown data is an anomaly instance is determined by their location with respect to the boundary.…”

Section: Conventional Anomaly Detectionmentioning

confidence: 99%

adVAE: A self-adversarial variational autoencoder with Gaussian anomaly prior knowledge for anomaly detection

Wang

Lin

et al. 2020

Knowledge-Based Systems

100

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Recently, deep generative models have become increasingly popular in unsupervised anomaly detection. However, deep generative models aim at recovering the data distribution rather than detecting anomalies. Moreover, deep generative models have the risk of overfitting training samples, which has disastrous effects on anomaly detection performance. To solve the above two problems, we propose a self-adversarial variational autoencoder (adVAE) with a Gaussian anomaly prior assumption. We assume that both the anomalous and the normal prior distribution are Gaussian and have overlaps in the latent space. Therefore, a Gaussian transformer net T is trained to synthesize anomalous but near-normal latent variables. Keeping the original training objective of a variational autoencoder, a generator G tries to distinguish between the normal latent variables encoded by E and the anomalous latent variables synthesized by T, and the encoder E is trained to discriminate whether the output of G is real. These new objectives we added not only give both G and E the ability to discriminate, but also become an additional regularization mechanism to prevent overfitting. Compared with other competitive methods, the proposed model achieves significant improvements in extensive experiments. The employed datasets and our model are available in a Github repository.

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“…The benifit of OCSVCs over other state-of-the-art OCC techniques is its work-ability with only positive class samples whereas the other methods need negative class samples too for smooth operation, hence the OCSVCs are found more suitable for this research. Based on extensive literature review, it is evident that OCSVMs (a type of OCSVC) are mostly used for novelty/anomaly detection in various application domains such as intrusion detection [20] , [27] , fraud detection [10] , [15] disease diagnosis [8] , [43] novelty detection [46] and document classification [28] . These assorted applicability make OCSVMs very interesting and important.…”

Section: Introductionmentioning

confidence: 99%

Target specific mining of COVID-19 scholarly articles using one-class approach

Sonbhadra

Agarwal

Nagabhushan

2020

Chaos, Solitons & Fractals

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Highlights In the presence of numerous research articles, extracting best-suited articles is timeconsuming and manually impractical. The objective of this paper is to extract the activity and trends of coronavirus related research articles using machine learning approaches to help the research community for future exploration concerning COVID-19 prevention and treatment techniques. The COVID-19 open research dataset (CORD-19) is used for experiments, whereas several target-tasks along with explanations are defined for classification, based on domain knowledge. Clustering techniques are used to create the different clusters of available articles, and later the task assignment is performed using parallel one-class support vector machines (OCSVMs). These defined tasks describes the behavior of clusters to accomplish targetclass guided mining. Experiments with original and reduced features validate the performance of the approach. It is evident that the k-means clustering algorithm, followed by parallel OCSVMs, outperforms other methods for both original and reduced feature space.

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Active learning based support vector data description method for robust novelty detection

Cited by 45 publications

References 15 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

adVAE: A self-adversarial variational autoencoder with Gaussian anomaly prior knowledge for anomaly detection

Target specific mining of COVID-19 scholarly articles using one-class approach

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