Learning Ensembles of Anomaly Detectors on Synthetic Data

Smolyakov, Dmitry; Sviridenko, Nadezda; Ishimtsev, Vladislav; Burikov, Evgeny; Burnaev, Evgeny

doi:10.48550/arxiv.1905.07892

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…The classes were highly skewed, with only 89 recurrent strokes recorded within 1 year (7.49%). Five machine learning approaches were deployed to handle this: random undersampling of the majority class, 21 synthetic oversampling of the minority class using the Synthetic Minority Oversampling Technique algorithm, 21 cost-sensitive learning 21 (assigning higher penalty to misclassification of the minority class, COST), anomaly detection algorithms (treating minority class as an anomaly to be detected), 22 and balanced learning algorithms, 23 where class balancing is embedded in the learning algorithm.…”

Section: Model Development and Evaluationmentioning

confidence: 99%

Prediction of Recurrent Ischemic Stroke Using Registry Data and Machine Learning Methods: The Erlangen Stroke Registry

Vodenčarević

Weingärtner

Jaime

et al. 2022

Stroke

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BACKGROUND: There have been multiple efforts toward individual prediction of recurrent strokes based on structured clinical and imaging data using machine learning algorithms. Some of these efforts resulted in relatively accurate prediction models. However, acquiring clinical and imaging data is typically possible at provider sites only and is associated with additional costs. Therefore, we developed recurrent stroke prediction models based solely on data easily obtained from the patient at home. METHODS: Data from 384 patients with ischemic stroke were obtained from the Erlangen Stroke Registry. Patients were followed at 3 and 12 months after first stroke and then annually, for about 2 years on average. Multiple machine learning algorithms were applied to train predictive models for estimating individual risk of recurrent stroke within 1 year. Double nested cross-validation was utilized for conservative performance estimation and models’ learning capabilities were assessed by learning curves. Predicted probabilities were calibrated, and relative variable importance was assessed using explainable artificial intelligence techniques. RESULTS: The best model achieved the area under the curve of 0.70 (95% CI, 0.64–0.76) and relatively good probability calibration. The most predictive factors included patient’s family and housing circumstances, rehabilitative measures, age, high calorie diet, systolic and diastolic blood pressures, percutaneous endoscopic gastrotomy, number of family doctor’s home visits, and patient’s mental state. CONCLUSIONS: Developing fairly accurate models for individual risk prediction of recurrent ischemic stroke within 1 year solely based on registry data is feasible. Such models could be applied in a home setting to provide an initial risk assessment and identify high-risk patients early.

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Section: Model Development and Evaluationmentioning

confidence: 99%

Prediction of Recurrent Ischemic Stroke Using Registry Data and Machine Learning Methods: The Erlangen Stroke Registry

Vodenčarević

Weingärtner

Jaime

et al. 2022

Stroke

View full text Add to dashboard Cite

show abstract

“…For time series data, however, artificial anomalies and related data augmentation techniques have not been studied extensively. Smolyakov et al (2019) used artificial anomalies to select thresholds in ensembles of anomaly detection models. Most closely related to our approach, SR- CNN Ren et al (2019) trains a supervised CNN on top of an unsupervised anomaly detection model (SR), by using labels from injected single point outliers.…”

Section: −(1 − Ymentioning

confidence: 99%

Neural Contextual Anomaly Detection for Time Series

Carmona¹,

Aubet²,

Flunkert³

et al. 2021

Preprint

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We introduce Neural Contextual Anomaly Detection (NCAD), a framework for anomaly detection on time series that scales seamlessly from the unsupervised to supervised setting, and is applicable to both univariate and multivariate time series. This is achieved by effectively combining recent developments in representation learning for multivariate time series, with techniques for deep anomaly detection originally developed for computer vision that we tailor to the time series setting. Our window-based approach facilitates learning the boundary between normal and anomalous classes by injecting generic synthetic anomalies into the available data. Moreover, our method can effectively take advantage of all the available information, be it as domain knowledge, or as training labels in the semi-supervised setting. We demonstrate empirically on standard benchmark datasets that our approach obtains a state-of-the-art performance in these settings. * Equal contribution. † Work done while working at AWS AI Labs.Preprint. Under review.

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Adversarial Explanations for Knowledge Graph Embeddings

Carmona

Aubet

Flunkert

et al. 2022

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence

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We introduce Neural Contextual Anomaly Detection (NCAD), a framework for anomaly detection on time series that scales seamlessly from the unsupervised to supervised setting, and is applicable to both univariate and multivariate time series. This is achieved by combining recent developments in representation learning for multivariate time series, with techniques for deep anomaly detection originally developed for computer vision that we tailor to the time series setting. Our window-based approach facilitates learning the boundary between normal and anomalous classes by injecting generic synthetic anomalies into the available data. NCAD can effectively take advantage of domain knowledge and of any available training labels. We demonstrate empirically on standard benchmark datasets that our approach obtains a state-of-the-art performance in the supervised, semi-supervised, and unsupervised settings.

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Learning Ensembles of Anomaly Detectors on Synthetic Data

Cited by 3 publications

References 18 publications

Prediction of Recurrent Ischemic Stroke Using Registry Data and Machine Learning Methods: The Erlangen Stroke Registry

Prediction of Recurrent Ischemic Stroke Using Registry Data and Machine Learning Methods: The Erlangen Stroke Registry

Neural Contextual Anomaly Detection for Time Series

Adversarial Explanations for Knowledge Graph Embeddings

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