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 676–700 of 703 papers

TitleStatusHype
SSB: Simple but Strong Baseline for Boosting Performance of Open-Set Semi-Supervised LearningCode0
Anomaly Detection in Networks via Score-Based Generative ModelsCode0
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier DetectionCode0
Towards Predicting the Quality of Red Wine Using Novel Machine Learning Methods for Classification, Data Visualization and AnalysisCode0
Learning to Classify Open Intent via Soft Labeling and Manifold MixupCode0
Unsupervised Boosting-based Autoencoder Ensembles for Outlier DetectionCode0
G-PECNet: Towards a Generalizable Pedestrian Trajectory Prediction SystemCode0
A geometric perspective on functional outlier detectionCode0
Event Outlier Detection in Continuous TimeCode0
Statistical Test for Feature Selection Pipelines by Selective InferenceCode0
Robust Ordinal Embedding from Contaminated Relative ComparisonsCode0
Robust Outlier Arm IdentificationCode0
Outlier-Insensitive Kalman Filtering: Theory and ApplicationsCode0
Local Subspace-Based Outlier Detection using Global NeighbourhoodsCode0
MaxGap Bandit: Adaptive Algorithms for Approximate RankingCode0
STWalk: Learning Trajectory Representations in Temporal GraphsCode0
LSCP: Locally Selective Combination in Parallel Outlier EnsemblesCode0
Consistent and Flexible Selectivity Estimation for High-Dimensional DataCode0
Conformal inference is (almost) free for neural networks trained with early stoppingCode0
A Method of Moments Embedding Constraint and its Application to Semi-Supervised LearningCode0
Condition Number Analysis of Kernel-based Density Ratio EstimationCode0
Robust Data Clustering with Outliers via Transformed Tensor Low-Rank RepresentationCode0
Robust Spatiotemporal Epidemic Modeling with Integrated Adaptive Outlier DetectionCode0
CoMadOut -- A Robust Outlier Detection Algorithm based on CoMADCode0
ALDI++: Automatic and parameter-less discord and outlier detection for building energy load profilesCode0
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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