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 526–550 of 703 papers

TitleStatusHype
Positive Difference Distribution for Image Outlier Detection using Normalizing Flows and Contrastive Data—0
Anomaly Detection with HMM Gauge Likelihood Analysis—0
Anomaly-Injected Deep Support Vector Data Description for Text Outlier Detection—0
Anomaly Rule Detection in Sequence Data—0
A Non-Parametric Control Chart For High Frequency Multivariate Data—0
AI-enabled Blockchain: An Outlier-aware Consensus Protocol for Blockchain-based IoT Networks—0
An Outlier Detection-based Tree Selection Approach to Extreme Pruning of Random Forests—0
Applications of a Graph Theoretic Based Clustering Framework in Computer Vision and Pattern Recognition—0
Applications of Data Mining Techniques for Vehicular Ad hoc Networks—0
A Practical Algorithm for Distributed Clustering and Outlier Detection—0
A probabilistic view on Riemannian machine learning models for SPD matrices—0
Symbiotic Hybrid Neural Network Watchdog For Outlier Detection—0
A Rank-Based Similarity Metric for Word Embeddings—0
A refined convergence analysis of pDCA_e with applications to simultaneous sparse recovery and outlier detection—0
Are Outlier Detection Methods Resilient to Sampling?—0
Are Out-of-Distribution Detection Methods Effective on Large-Scale Datasets?—0
A Review of Change of Variable Formulas for Generative Modeling—0
A Review of Graph-Powered Data Quality Applications for IoT Monitoring Sensor Networks—0
A review on outlier/anomaly detection in time series data—0
A Robust AUC Maximization Framework with Simultaneous Outlier Detection and Feature Selection for Positive-Unlabeled Classification—0
A Robust Framework for Classifying Evolving Document Streams in an Expert-Machine-Crowd Setting—0
A Robust Learning Algorithm for Regression Models Using Distributionally Robust Optimization under the Wasserstein Metric—0
A Robust Regression Approach for Robot Model Learning—0
A Scalable Approach for Outlier Detection in Edge Streams Using Sketch-based Approximations—0
A Secure Clustering Protocol with Fuzzy Trust Evaluation and Outlier Detection for Industrial Wireless Sensor Networks—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