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 621630 of 703 papers

TitleStatusHype
Efficient variational Bayesian neural network ensembles for outlier detectionCode0
Deep SetsCode1
Outlier Cluster Formation in Spectral Clustering0
Interpreting Outliers: Localized Logistic Regression for Density Ratio Estimation0
Outlier Detection for Text Data : An Extended VersionCode0
Meta-Unsupervised-Learning: A supervised approach to unsupervised learning0
Detecting Unusual Input-Output Associations in Multivariate Conditional Data0
A General Framework for Density Based Time Series Clustering Exploiting a Novel Admissible Pruning Strategy0
Cognitive Deep Machine Can Train Itself0
Robust k-means: a Theoretical Revisit0
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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