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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 151–175 of 703 papers

TitleStatusHype
Incremental Data-driven Optimization of Complex Systems in Nonstationary Environments—0
Deep Learning for RF Signal Classification in Unknown and Dynamic Spectrum Environments—0
Applications of a Graph Theoretic Based Clustering Framework in Computer Vision and Pattern Recognition—0
A Local Density-Based Approach for Local Outlier Detection—0
Deep-Anomaly: Fully Convolutional Neural Network for Fast Anomaly Detection in Crowded Scenes—0
Comparative Study of Neighbor-based Methods for Local Outlier Detection—0
Adaptive PCA-Based Outlier Detection for Multi-Feature Time Series in Space Missions—0
Community-based anomaly detection using spectral graph filtering—0
Combining Structured and Unstructured Randomness in Large Scale PCA—0
An Outlier Detection-based Tree Selection Approach to Extreme Pruning of Random Forests—0
About Test-time training for outlier detection—0
Comparison of Outlier Detection Techniques for Structured Data—0
Comparison of Visual Trackers for Biomechanical Analysis of Running—0
Capturing the Denoising Effect of PCA via Compression Ratio—0
Deep Learning with Sets and Point Clouds—0
Concept-based Anomaly Detection in Retail Stores for Automatic Correction using Mobile Robots—0
Concept Learning through Deep Reinforcement Learning with Memory-Augmented Neural Networks—0
Conditional Selective Inference for Robust Regression and Outlier Detection using Piecewise-Linear Homotopy Continuation—0
Conditional Testing based on Localized Conformal p-values—0
A Practical Algorithm for Distributed Clustering and Outlier Detection—0
A probabilistic view on Riemannian machine learning models for SPD matrices—0
Conformal Prediction with Cellwise Outliers: A Detect-then-Impute Approach—0
Deep Variational Semi-Supervised Novelty Detection—0
AI-enabled Blockchain: An Outlier-aware Consensus Protocol for Blockchain-based IoT Networks—0
Cognitive Deep Machine Can Train Itself—0
Show:102550
← PrevPage 7 of 29Next →
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