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 301–325 of 703 papers

TitleStatusHype
An Efficient Hashing-based Ensemble Method for Collaborative Outlier Detection—0
NEAR - Newborns EEG Artifact RemovalCode1
ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution FunctionsCode1
Anomaly Detection using Capsule Networks for High-dimensional Datasets—0
Understanding and Mitigating the Effect of Outliers in Fair RankingCode0
Federated Nearest Neighbor Classification with a Colony of Fruit-Flies: With SupplementCode0
Robust factored principal component analysis for matrix-valued outlier accommodation and detection—0
LUNAR: Unifying Local Outlier Detection Methods via Graph Neural NetworksCode1
Outlier Detection using AI: A Survey—0
OpenMatch: Open-Set Semi-supervised Learning with Open-set Consistency RegularizationCode1
Automatic Unsupervised Outlier Model Selection—0
Anomaly Rule Detection in Sequence Data—0
Outlier Detection for Trajectories via Flow-embeddingsCode0
Isolation forests: looking beyond tree depth—0
Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles -- Extended Version—0
Active Relation Discovery: Towards General and Label-aware OpenRE—0
Mathematical Models for Local Sensing Hashes—0
Automatically detecting anomalous exoplanet transitsCode0
Outlier Detection as Instance Selection Method for Feature Selection in Time Series Classification—0
Contextual Unsupervised Outlier Detection in Sequences—0
Real-time Wireless Transmitter Authorization: Adapting to Dynamic Authorized Sets with Information Retrieval—0
BAHP: Benchmark of Assessing Word Embeddings in Historical Portuguese—0
The magnitude vector of imagesCode0
Anomaly-Injected Deep Support Vector Data Description for Text Outlier Detection—0
TOD: GPU-accelerated Outlier Detection via Tensor OperationsCode1
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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