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

Showing 401425 of 703 papers

TitleStatusHype
Sampling Method for Fast Training of Support Vector Data Description0
Scalable Support Vector Clustering Using Budget0
Self-Optimizing Feature Transformation0
Semantic Driven Energy based Out-of-Distribution Detection0
Semi-supervised Outlier Detection using Generative And Adversary Framework0
Separability and Scatteredness (S&S) Ratio-Based Efficient SVM Regularization Parameter, Kernel, and Kernel Parameter Selection0
Sequential Ensemble Learning for Outlier Detection: A Bias-Variance Perspective0
Sequential Outlier Detection based on Incremental Decision Trees0
Shaping Level Sets with Submodular Functions0
Similarity- based approach for outlier detection0
Simple robust genomic prediction and outlier detection for a multi-environmental field trial0
Single Channel Speech Enhancement Using Outlier Detection0
Size Matters: Cardinality-Constrained Clustering and Outlier Detection via Conic Optimization0
Spatiotemporal Data Mining: A Survey on Challenges and Open Problems0
SPINEX: Similarity-based Predictions with Explainable Neighbors Exploration for Anomaly and Outlier Detection0
SSDBCODI: Semi-Supervised Density-Based Clustering with Outliers Detection Integrated0
STACC, OOV Density and N-gram Saturation: Vicomtech's Participation in the WMT 2018 Shared Task on Parallel Corpus Filtering0
Statistical Outlier Identification in Multi-robot Visual SLAM using Expectation Maximization0
Structured Group Sparsity: A Novel Indoor WLAN Localization, Outlier Detection, and Radio Map Interpolation Scheme0
Suppressing Outlier Reconstruction in Autoencoders for Out-of-Distribution Detection0
Synthetic Data Generation and Automated Multidimensional Data Labeling for AI/ML in General and Circular Coordinates0
Synthetic outlier generation for anomaly detection in autonomous driving0
Tax Evasion Risk Management Using a Hybrid Unsupervised Outlier Detection Method0
Technical outlier detection via convolutional variational autoencoder for the ADMANI breast mammogram dataset0
Temporal Analysis of Adversarial Attacks in Federated Learning0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1VRAE+SVMAccuracy0.98Unverified
2F-t ALSTM-FCNAccuracy0.95Unverified
3GENDISAccuracy0.94Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy99.03Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy37.62Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy65.6Unverified
#ModelMetricClaimedVerifiedStatus
1PAEAUROC1Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy99.05Unverified
#ModelMetricClaimedVerifiedStatus
1MIXAUC0.86Unverified
#ModelMetricClaimedVerifiedStatus
1MIXAUC-ROC0.85Unverified
#ModelMetricClaimedVerifiedStatus
1MIXAUC-ROC0.93Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy86.33Unverified
#ModelMetricClaimedVerifiedStatus
1LSTMCapsAverage F10.74Unverified