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 351–375 of 703 papers

TitleStatusHype
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
Outlier Detection as Instance Selection Method for Feature Selection in Time Series Classification—0
Automatically detecting anomalous exoplanet transitsCode0
Mathematical Models for Local Sensing Hashes—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
News-based Business Sentiment and its Properties as an Economic Index—0
Large Scale Substitution-based Word Sense Induction—0
C-AllOut: Catching & Calling Outliers by Type—0
Distance Based Pattern Driven Mining for Outlier Detection in High Dimensional Big Dataset—0
Probabilistic Robust Autoencoders for Outlier Detection—0
When Complexity Is Good: Do We Need Recurrent Deep Learning For Time Series Outlier Detection?—0
Learning to Rank Anomalies: Scalar Performance Criteria and Maximization of Two-Sample Rank Statistics—0
A geometric perspective on functional outlier detectionCode0
Cascade Watchdog: A Multi-tiered Adversarial Guard for Outlier Detection—0
Robust outlier detection by de-biasing VAE likelihoods—0
Out-of-Distribution Detection Using Outlier Detection MethodsCode0
Discovering outliers in the Mars Express thermal power consumption patterns—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