SOTAVerified

Outlier Detection

Outlier Detection is a task of identifying a subset of a given data set which are considered anomalous in that they are unusual from other instances. It is one of the core data mining tasks and is central to many applications. In the security field, it can be used to identify potentially threatening users, in the manufacturing field it can be used to identify parts that are likely to fail.

Source: Coverage-based Outlier Explanation

Papers

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Showing 301–325 of 703 papers

TitleStatusHype
Weighted Scaling Approach for Metabolomics Data AnalysisCode0
3D Labeling Tool—0
An Evolutionary Game based Secure Clustering Protocol with Fuzzy Trust Evaluation and Outlier Detection for Wireless Sensor Networks—0
A Secure Clustering Protocol with Fuzzy Trust Evaluation and Outlier Detection for Industrial Wireless Sensor Networks—0
Outlier Explanation via Sum-Product Networks—0
Repairing Systematic Outliers by Learning Clean Subspaces in VAEsCode0
Outlier detection of vital sign trajectories from COVID-19 patientsCode0
Suppressing Poisoning Attacks on Federated Learning for Medical ImagingCode0
A geometric framework for outlier detection in high-dimensional data—0
K-ARMA Models for Clustering Time Series Data—0
BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed GraphsCode0
Hyperparameter Sensitivity in Deep Outlier Detection: Analysis and a Scalable Hyper-Ensemble SolutionCode0
Improving Solar Flare Prediction by Time Series Outlier Detection—0
What do we learn? Debunking the Myth of Unsupervised Outlier Detection—0
Robust Time Series Dissimilarity Measure for Outlier Detection and Periodicity Detection—0
Robust Flow-based Conformal Inference (FCI) with Statistical Guarantee—0
Detect Professional Malicious User with Metric Learning in Recommender Systems—0
ODBO: Bayesian Optimization with Search Space Prescreening for Directed Protein EvolutionCode0
Defending Object Detectors against Patch Attacks with Out-of-Distribution Smoothing—0
Deep Sequence Modeling for Anomalous ISP Traffic Prediction—0
Towards an Ensemble Regressor Model for Anomalous ISP Traffic Prediction—0
Improving the Robustness of Federated Learning for Severely Imbalanced Datasets—0
Performance Analysis of Out-of-Distribution Detection on Trained Neural Networks—0
Capturing the Denoising Effect of PCA via Compression Ratio—0
Fluctuation-based Outlier DetectionCode0
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All datasetsECG5000Balance scale_class 1Breast cancer Wisconsin_class 2Breast cancer Wisconsin_class 4Fashion-MNISTGlass identificationHeart-CHepatitisInternet AdIonosphere_class bSKAB

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1VRAE+SVMAccuracy0.98—Unverified
2F-t ALSTM-FCNAccuracy0.95—Unverified
3GENDISAccuracy0.94—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy99.03—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy37.62—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy65.6—Unverified
#ModelMetricClaimedVerifiedStatus
1PAEAUROC1—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy99.05—Unverified
#ModelMetricClaimedVerifiedStatus
1MIXAUC0.86—Unverified
#ModelMetricClaimedVerifiedStatus
1MIXAUC-ROC0.85—Unverified
#ModelMetricClaimedVerifiedStatus
1MIXAUC-ROC0.93—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy86.33—Unverified
#ModelMetricClaimedVerifiedStatus
1LSTMCapsAverage F10.74—Unverified