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
A Secure Clustering Protocol with Fuzzy Trust Evaluation and Outlier Detection for Industrial Wireless Sensor Networks—0
Deep Sequence Modeling for Anomalous ISP Traffic Prediction—0
A Scalable Approach for Outlier Detection in Edge Streams Using Sketch-based Approximations—0
A Robust Regression Approach for Robot Model Learning—0
An Approximate Bayesian Long Short-Term Memory Algorithm for Outlier Detection—0
Deep Learning with Sets and Point Clouds—0
Deep Learning for RF Signal Classification in Unknown and Dynamic Spectrum Environments—0
A Robust Learning Algorithm for Regression Models Using Distributionally Robust Optimization under the Wasserstein Metric—0
Deep Learning for Anomaly Detection: A Review—0
A Robust Framework for Classifying Evolving Document Streams in an Expert-Machine-Crowd Setting—0
Analyzing categorical time series with the R package ctsfeatures—0
Deep-Anomaly: Fully Convolutional Neural Network for Fast Anomaly Detection in Crowded Scenes—0
Decision-change Informed Rejection Improves Robustness in Pattern Recognition-based Myoelectric Control—0
Dealing with Class Imbalance using Thresholding—0
A Robust AUC Maximization Framework with Simultaneous Outlier Detection and Feature Selection for Positive-Unlabeled Classification—0
Analysis of Learning from Positive and Unlabeled Data—0
A deep mixture density network for outlier-corrected interpolation of crowd-sourced weather data—0
A Comprehensive System for Secondary Structure Analysis of Protein Models—0
3D Labeling Tool—0
Data Stream Clustering: A Review—0
Data refinement for fully unsupervised visual inspection using pre-trained networks—0
A review on outlier/anomaly detection in time series data—0
Data Enrichment Opportunities for Distribution Grid Cable Networks using Variational Autoencoders—0
A Review of Graph-Powered Data Quality Applications for IoT Monitoring Sensor Networks—0
A multi-stream deep neural network with late fuzzy fusion for real-world anomaly detection—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