Voting-Based Ensemble of Unsupervised Outlier Detectors

Thomas, Roy; Judith, J. E.

doi:10.1007/978-981-15-3992-3_42

Cited by 11 publications

(9 citation statements)

References 8 publications

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“…Novelty detection algorithms generally consist of unsupervised or semisupervised learning problems, depending on the technique used and the nature of the training dataset [94][95][96]. Such methods have been applied to many data types, including time series [97][98][99].…”

Section: Novelty Detection Algorithmsmentioning

confidence: 99%

Toward Workable and Cost-Efficient Monitoring of Unstable Rock Compartments with Ambient Noise

Bottelin¹,

Baillet

Carrier³

et al. 2021

Geosciences

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Ambient Vibration-Based Structural Health Monitoring (AVB–SHM) studies on prone-to-fall rock compartments have recently succeeded in detecting both pre-failure damaging processes and reinforcement provided by bolting. The current AVB–SHM instrumentation layout is yet generally an overkill, creating cost and power issues and sometimes requiring advanced signal processing techniques. In this article, we paved the way toward an innovative edge-computing approach tested on ambient vibration records made during the bolting of a ~760 m3 limestone rock column (Vercors, France). First, we established some guidelines for prone-to-fall rock column AVB–SHM by comparing several basic, computing-efficient, seismic parameters (i.e., Fast Fourier Transform, Horizontal to Vertical and Horizontal to Horizontal Spectral Ratios). All three parameters performed well in revealing the unstable compartment’s fundamental resonance frequency. HHSR appeared as the most consistent spectral estimator, succeeding in revealing both the fundamental and higher modes. Only the fundamental mode should be trustfully monitored with HVSR since higher peaks may be artifacts. Then, the first application of a novelty detection algorithm on an unstable rock column AVB–SHM case study showed the following: the feasibility of automatic removing the adverse thermomechanical fluctuations in column’s dynamic parameters based on machine learning, as well as the systematic detection of clear, permanent change in column’s dynamic behavior after grout injection and hardening around the bolts (i1 and i2). This implementation represents a significant workload reduction, compared to physical-based algorithms or numerical twin modeling, and shows better robustness with regard to instrumentation gaps. We believe that edge-computing monitoring systems combining basic seismic signal processing techniques and automatic detection algorithms could help facilitate AVB–SHM of remote natural structures such as prone-to-fall rock compartments.

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Section: Novelty Detection Algorithmsmentioning

confidence: 99%

Toward Workable and Cost-Efficient Monitoring of Unstable Rock Compartments with Ambient Noise

Bottelin¹,

Baillet

Carrier³

et al. 2021

Geosciences

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“…Machine learning-based techniques used for outlier detection in non-sequence data, such as support vector machine (SVM) [12], local outlier factor (LOF) [13], isolation forest (IF) [14], and elliptic envelope (EE) [15] have been used to detect anomalous records from time series data [27]. Such approaches do not consider temporal dependencies between data records and can only detect trivial out-of-range outliers.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, outlier detection using machine learning fails to explain how records are anomalous to the domain experts. Finally, most works [12][13][14][15][16][17] identifying anomalous records in a data set cannot be used on time-series data as anomalies may span multiple attributes and records in a sequence [18]. We aim to eliminate the above shortcomings by detecting anomalies in COVID-19 time-series data without having access to labeled data and explaining the anomalies to domain experts in a comprehensible manner.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection in COVID-19 Time-Series Data

et al. 2021

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Anomaly detection and explanation in big volumes of real-world medical data, such as those pertaining to COVID-19, pose some challenges. First, we are dealing with time-series data. Typical time-series data describe behavior of a single object over time. In medical data, we are dealing with time-series data belonging to multiple entities. Thus, there may be multiple subsets of records such that records in each subset, which belong to a single entity are temporally dependent, but the records in different subsets are unrelated. Moreover, the records in a subset contain different types of attributes, some of which must be grouped in a particular manner to make the analysis meaningful. Anomaly detection techniques need to be customized for time-series data belonging to multiple entities. Second, anomaly detection techniques fail to explain the cause of outliers to the experts. This is critical for new diseases and pandemics where current knowledge is insufficient. We propose to address these issues by extending our existing work called IDEAL, which is an LSTM-autoencoder based approach for data quality testing of sequential records, and provides explanations of constraint violations in a manner that is understandable to end-users. The extension (1) uses a novel two-level reshaping technique that splits COVID-19 data sets into multiple temporally-dependent subsequences and (2) adds a data visualization plot to further explain the anomalies and evaluate the level of abnormality of subsequences detected by IDEAL. We performed two systematic evaluation studies for our anomalous subsequence detection. One study uses aggregate data, including the number of cases, deaths, recovered, and percentage of hospitalization rate, collected from a COVID tracking project, New York Times, and Johns Hopkins for the same time period. The other study uses COVID-19 patient medical records obtained from Anschutz Medical Center health data warehouse. The results are promising and indicate that our techniques can be used to detect anomalies in large volumes of real-world unlabeled data whose accuracy or validity is unknown.

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“…This treebased model is based on the principle that the fewer instances of anomalies generate a smaller number of partitions, and thus are likely to have short paths in the tree structure [63]. Other models reported in the literature involve the use of random forests [64], gradient boosted machines [65], artificial neural networks [66], or voting ensembles [67]. Models from the second group have multiple configurations, varying the generative methods and outlier detection criteria.…”

Section: Time Series Anomaly Detectionmentioning

confidence: 99%

Explaining Bad Forecasts in Global Time Series Models

et al. 2021

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While increasing empirical evidence suggests that global time series forecasting models can achieve better forecasting performance than local ones, there is a research void regarding when and why the global models fail to provide a good forecast. This paper uses anomaly detection algorithms and explainable artificial intelligence (XAI) to answer when and why a forecast should not be trusted. To address this issue, a dashboard was built to inform the user regarding (i) the relevance of the features for that particular forecast, (ii) which training samples most likely influenced the forecast outcome, (iii) why the forecast is considered an outlier, and (iv) provide a range of counterfactual examples to understand value changes, in the feature vector or the predicted value, can lead to a different outcome. Moreover, a modular architecture and a methodology were developed to iteratively remove noisy data instances from the train set, to enhance the overall global time series forecasting model performance. Finally, to test the effectiveness of the proposed approach, it was validated on two publicly available real-world datasets.

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Voting-Based Ensemble of Unsupervised Outlier Detectors

Cited by 11 publications

References 8 publications

Toward Workable and Cost-Efficient Monitoring of Unstable Rock Compartments with Ambient Noise

Toward Workable and Cost-Efficient Monitoring of Unstable Rock Compartments with Ambient Noise

Anomaly Detection in COVID-19 Time-Series Data

Explaining Bad Forecasts in Global Time Series Models

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