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 651–675 of 703 papers

TitleStatusHype
Holistic Features For Real-Time Crowd Behaviour Anomaly Detection—0
Sampling Method for Fast Training of Support Vector Data Description—0
A Scalable Approach for Outlier Detection in Edge Streams Using Sketch-based Approximations—0
Single Channel Speech Enhancement Using Outlier Detection—0
Linear-time Outlier Detection via Sensitivity—0
Distance for Functional Data Clustering Based on Smoothing Parameter Commutation—0
Depth-Based Object Tracking Using a Robust Gaussian FilterCode0
Peak Criterion for Choosing Gaussian Kernel Bandwidth in Support Vector Data Description—0
GraphPrints: Towards a Graph Analytic Method for Network Anomaly Detection—0
Outlier Detection In Large-scale Traffic Data By Naïve Bayes Method and Gaussian Mixture Model Method—0
Robust Classification by Pre-conditioned LASSO and Transductive Diffusion Component Analysis—0
A Framework for Clustering Uncertain Data—0
Learning Representations for Outlier Detection on a Budget—0
Differentially Private Analysis of Outliers—0
Diffusion Nets—0
MCODE: Multivariate Conditional Outlier Detection—0
An Outlier Detection-based Tree Selection Approach to Extreme Pruning of Random Forests—0
Random Subspace Learning Approach to High-Dimensional Outliers Detection—0
Robust Subjective Visual Property Prediction from Crowdsourced Pairwise Labels—0
On Integrated Clustering and Outlier Detection—0
Analysis of Learning from Positive and Unlabeled Data—0
Similarity- based approach for outlier detection—0
Motion Estimation via Robust Decomposition with Constrained Rank—0
Breakdown Point of Robust Support Vector Machine—0
Evaluating Visual Properties via Robust HodgeRank—0
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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