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 476–500 of 703 papers

TitleStatusHype
K-ARMA Models for Clustering Time Series Data—0
Kernel Bandwidth Selection for SVDD: Peak Criterion Approach for Large Data—0
Kernel-based Outlier Detection using the Inverse Christoffel Function—0
Kernel Random Projection Depth for Outlier Detection—0
KNN Ensembles for Tweedie Regression: The Power of Multiscale Neighborhoods—0
K-NS: Section-Based Outlier Detection in High Dimensional Space—0
A Simple and Fast Algorithm for L1-norm Kernel PCA—0
Large-scale gradient-based training of Mixtures of Factor Analyzers—0
Large Scale Substitution-based Word Sense Induction—0
LayerIF: Estimating Layer Quality for Large Language Models using Influence Functions—0
Learning Geometric Concepts with Nasty Noise—0
Learning Joint Latent Space EBM Prior Model for Multi-layer Generator—0
Learning low-dimensional manifolds under the L0-norm constraint for unsupervised outlier detection—0
Learning Markerless Robot-Depth Camera Calibration and End-Effector Pose Estimation—0
Learning novel representations of variable sources from multi-modal Gaia data via autoencoders—0
Learning Representations for Outlier Detection on a Budget—0
Learning Representations from Healthcare Time Series Data for Unsupervised Anomaly Detection—0
LEARNING SEMANTIC WORD RESPRESENTATIONS VIA TENSOR FACTORIZATION—0
Learning to Detect Interesting Anomalies—0
Learning to Rank Anomalies: Scalar Performance Criteria and Maximization of Two-Sample Rank Statistics—0
Learning Universe Model for Partial Matching Networks over Multiple Graphs—0
Let me join you! Real-time F-formation recognition by a socially aware robot—0
Leveraging Ensemble-Based Semi-Supervised Learning for Illicit Account Detection in Ethereum DeFi Transactions—0
Lifted Coefficient of Determination: Fast model-free prediction intervals and likelihood-free model comparison—0
Linear-time Outlier Detection via Sensitivity—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