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 476–500 of 703 papers

TitleStatusHype
3D Scanning: A Comprehensive Survey—0
A Bayesian Ensemble for Unsupervised Anomaly Detection—0
A boosted outlier detection method based on the spectrum of the Laplacian matrix of a graph—0
About Test-time training for outlier detection—0
Achieving differential privacy for k-nearest neighbors based outlier detection by data partitioning—0
A Comprehensive System for Secondary Structure Analysis of Protein Models—0
Active Learning of SVDD Hyperparameter Values—0
Active Relation Discovery: Towards General and Label-aware OpenRE—0
Active Relation Discovery: Towards General and Label-aware Open Relation Extraction—0
Adaptive Double-Exploration Tradeoff for Outlier Detection—0
Adaptive Outlier Detection for Power MOSFETs Based on Gaussian Process Regression—0
Adaptive PCA-Based Outlier Detection for Multi-Feature Time Series in Space Missions—0
Incremental Data-driven Optimization of Complex Systems in Nonstationary Environments—0
A Deep Learning Anomaly Detection Method in Textual Data—0
A deep mixture density network for outlier-corrected interpolation of crowd-sourced weather data—0
A feature construction framework based on outlier detection and discriminative pattern mining—0
A Framework for Clustering Uncertain Data—0
A Framework for Developing and Evaluating Word Embeddings of Drug-named Entity—0
A General Framework for Density Based Time Series Clustering Exploiting a Novel Admissible Pruning Strategy—0
A geometric framework for outlier detection in high-dimensional data—0
A Hybrid Deep Feature-Based Deformable Image Registration Method for Pathology Images—0
A Hybrid Intelligent Framework for Maximising SAG Mill Throughput: An Integration of Expert Knowledge, Machine Learning and Evolutionary Algorithms for Parameter Optimisation—0
A Joint Indoor WLAN Localization and Outlier Detection Scheme Using LASSO and Elastic-Net Optimization Techniques—0
A Large-scale Study on Unsupervised Outlier Model Selection: Do Internal Strategies Suffice?—0
A Local Density-Based Approach for Local Outlier Detection—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