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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 376–400 of 703 papers

TitleStatusHype
REMIX: Automated Exploration for Interactive Outlier Detection—0
Robust and Explainable Autoencoders for Unsupervised Time Series Outlier Detection---Extended Version—0
Robust Anomaly Detection and Backdoor Attack Detection Via Differential Privacy—0
Robust Classification by Pre-conditioned LASSO and Transductive Diffusion Component Analysis—0
Robust Contextual Outlier Detection: Where Context Meets Sparsity—0
Robust factored principal component analysis for matrix-valued outlier accommodation and detection—0
Robust Flow-based Conformal Inference (FCI) with Statistical Guarantee—0
Robust Indoor Localization via Conformal Methods and Variational Bayesian Adaptive Filtering—0
Robust k-means: a Theoretical Revisit—0
Robust Multi-Source Domain Adaptation under Label Shift—0
Robust Object Tracking with Crow Search Optimized Multi-cue Particle Filter—0
Robust Outlier Detection Technique in Data Mining: A Univariate Approach—0
Robust PCA and subspace tracking from incomplete observations using L0-surrogates—0
Robust Randomized Low-Rank Approximation with Row-Wise Outlier Detection—0
Evaluating Visual Properties via Robust HodgeRank—0
Robust Statistical Scaling of Outlier Scores: Improving the Quality of Outlier Probabilities for Outliers (Extended Version)—0
Robust Subjective Visual Property Prediction from Crowdsourced Pairwise Labels—0
Robust Subspace Outlier Detection in High Dimensional Space—0
Robust Time Series Dissimilarity Measure for Outlier Detection and Periodicity Detection—0
Robust Uncertainty Estimation for Classification of Maritime Objects—0
Robust Variational Autoencoder—0
RODD: Robust Outlier Detection in Data Cubes—0
Rotational Outlier Identification in Pose Graphs Using Dual Decomposition—0
Rotation Averaging with Attention Graph Neural Networks—0
Safeguarding against spurious AI-based predictions: The case of automated verbal memory assessment—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