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 226–250 of 703 papers

TitleStatusHype
Anomaly Detection in Networks via Score-Based Generative ModelsCode0
Cascade Subspace Clustering for Outlier Detection—0
Female mosquito detection by means of AI techniques inside release containers in the context of a Sterile Insect Technique program—0
Kernel Random Projection Depth for Outlier Detection—0
Learning Joint Latent Space EBM Prior Model for Multi-layer Generator—0
WePaMaDM-Outlier Detection: Weighted Outlier Detection using Pattern Approaches for Mass Data Mining—0
DEK-Forecaster: A Novel Deep Learning Model Integrated with EMD-KNN for Traffic Prediction—0
Hierarchical Multiresolution Feature- and Prior-based Graphs for Classification—0
GBG++: A Fast and Stable Granular Ball Generation Method for Classification—0
Unleashing the Potential of Unsupervised Deep Outlier Detection through Automated Training StoppingCode0
Centering the Margins: Outlier-Based Identification of Harmed Populations in Toxicity Detection—0
Technical outlier detection via convolutional variational autoencoder for the ADMANI breast mammogram dataset—0
Non-parametric cumulants approach for outlier detection of multivariate financial data—0
Incremental Outlier Detection Modelling Using Streaming Analytics in Finance & Health Care—0
Separability and Scatteredness (S&S) Ratio-Based Efficient SVM Regularization Parameter, Kernel, and Kernel Parameter Selection—0
A Probabilistic Transformation of Distance-Based OutliersCode0
Efficient Neural Network based Classification and Outlier Detection for Image Moderation using Compressed Sensing and Group Testing—0
Outlier galaxy images in the Dark Energy Survey and their identification with unsupervised machine learning—0
Two-phase Dual COPOD Method for Anomaly Detection in Industrial Control System—0
HPSCAN: Human Perception-Based Scattered Data ClusteringCode0
Analyzing categorical time series with the R package ctsfeatures—0
Ordinal time series analysis with the R package otsfeatures—0
One-Class SVM on siamese neural network latent space for Unsupervised Anomaly Detection on brain MRI White Matter Hyperintensities—0
An Improved Heart Disease Prediction Using Stacked Ensemble Method—0
PIKS: A Technique to Identify Actionable Trends for Policy-Makers Through Open Healthcare Data—0
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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