Novelty Detection with One-Class Support Vector Machines

Shawe‐Taylor, John; žličar, Blaž

doi:10.1007/978-3-319-17377-1_24

Cited by 11 publications

(5 citation statements)

References 2 publications

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“…data, which does not encompass typical time-series data. To address this, [6] and [7] convert time-series data into vectors to enable One-class SVM application.…”

Section: Approaches To Unsupervised Degradation Monitoringmentioning

confidence: 99%

Interpretability of Causal Discovery in Tracking Deterioration in a Highly Dynamic Process

Choudhary,

Vuković,

Mutlu

et al. 2024

Preprint

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In a dynamic production processes, mechanical degradation poses a significant challenge, impacting product quality and process efficiency. This paper explores a novel approach for monitoring degradation in the context of viscose fiber production, a highly dynamic manufacturing process. Using causal discovery techniques, our method allows domain experts to incorporate background knowledge into the creation of causal graphs. Further, it enhances the interpretability and increases the ability to identify potential problems via changes in causal relations over time. The case study employs a comprehensive analysis of the viscose fiber production process within a prominent textile industry, emphasizing the advantages of causal discovery for monitoring degradation. The results are compared with state-of-the-art methods, which are not considered to be interpretable, specifically LSTM-based autoencoders, showcasing the alignment and validation of our approach. This paper provides valuable information on degradation monitoring strategies, demonstrating the efficacy of causal discovery in dynamic manufacturing environments. The findings contribute to the evolving landscape of process optimization and quality control.

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“…data, which does not encompass typical time-series data. To address this, [6] and [7] convert time-series data into vectors to enable One-class SVM application.…”

Section: Approaches To Unsupervised Degradation Monitoringmentioning

confidence: 99%

Interpretability of Causal Discovery in Tracking Deterioration in a Highly Dynamic Process

Choudhary,

Vuković,

Mutlu

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…To address this, ref. [ 13 , 14 ] convert time series data into vectors to enable One-class SVM application.…”

Section: Background and Related Workmentioning

confidence: 99%

Interpretability of Causal Discovery in Tracking Deterioration in a Highly Dynamic Process

Choudhary,

Vuković,

Mutlu

et al. 2024

Sensors

View full text Add to dashboard Cite

In a dynamic production processes, mechanical degradation poses a significant challenge, impacting product quality and process efficiency. This paper explores a novel approach for monitoring degradation in the context of viscose fiber production, a highly dynamic manufacturing process. Using causal discovery techniques, our method allows domain experts to incorporate background knowledge into the creation of causal graphs. Further, it enhances the interpretability and increases the ability to identify potential problems via changes in causal relations over time. The case study employs a comprehensive analysis of the viscose fiber production process within a prominent textile industry, emphasizing the advantages of causal discovery for monitoring degradation. The results are compared with state-of-the-art methods, which are not considered to be interpretable, specifically LSTM-based autoencoder, UnSupervised Anomaly Detection on Multivariate Time Series (USAD), and Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data (TranAD), showcasing the alignment and validation of our approach. This paper provides valuable information on degradation monitoring strategies, demonstrating the efficacy of causal discovery in dynamic manufacturing environments. The findings contribute to the evolving landscape of process optimization and quality control.

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“…Therefore, we used the STCNN-RN combined with the GACG method to perform comparisons with other benchmark models. We selected the one-class SVM (OC-SVM) [40] and self-organizing maps (SOMs) [41] as the control group.…”

Section: Compare Different Alternative Modelsmentioning

confidence: 99%

“…Shawe-Taylor and Žličar [40] applied the OC-SVM to identify potential anomalies in financial time-series data and to find the distribution and the timing of the occurrence of the anomalous behavior in these data. The experiment results indicated that the OC-SVM detected changes in anomalous behavior in synthetic data sets and in several empirical data sets.…”

Section: Compare Different Alternative Modelsmentioning

confidence: 99%

Interpretable Stock Anomaly Detection Based on Spatio-Temporal Relation Networks With Genetic Algorithm

Cheong

Huang

2021

IEEE Access

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Instability in financial markets represents a considerable risk to investors; examples of instability include a market crash caused by systematic risks and abnormal stock price volatility caused by artificial hype. The early detection of abnormal behavior can help investors adjust their strategy and reduce investment risks. We proposed a spatiotemporal convolutional neural network-based relational network (STCNN-RN) model that can learn the complex correlations between multiple financial timeseries data sets, and we used genetic algorithms with a constrained gene to discover the time points for outlier companies by fitting the STCNN-RN model; we used these outlier points to identify abnormal situations. Most research on identifying anomalous patterns has been unable to sufficiently explain the reason for anomalies to investors. We applied an interpretability model to enable investors to understand these anomalous time points in relation to companies and discover the key factors giving rise to the anomalies. The experiment results revealed that the proposed model can be used to model multiple financial time-series data sets and to capture anomalous situations in relevant companies. Because this study explored the discovery of anomaly phenomena in all transaction data and the explanation of these abnormalities, investors can understand a stock market situation holistically.

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Novelty Detection with One-Class Support Vector Machines

Cited by 11 publications

References 2 publications

Interpretability of Causal Discovery in Tracking Deterioration in a Highly Dynamic Process

Interpretability of Causal Discovery in Tracking Deterioration in a Highly Dynamic Process

Interpretability of Causal Discovery in Tracking Deterioration in a Highly Dynamic Process

Interpretable Stock Anomaly Detection Based on Spatio-Temporal Relation Networks With Genetic Algorithm

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