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 501–550 of 703 papers

TitleStatusHype
ALRe: Outlier Detection for Guided Refinement—0
ALTBI: Constructing Improved Outlier Detection Models via Optimization of Inlier-Memorization Effect—0
A Meta-Learning Algorithm for Interrogative Agendas—0
A Model for Spatial Outlier Detection Based on Weighted Neighborhood Relationship—0
A multi-stream deep neural network with late fuzzy fusion for real-world anomaly detection—0
Analysis of Learning from Positive and Unlabeled Data—0
Analyzing categorical time series with the R package ctsfeatures—0
An Approximate Bayesian Long Short-Term Memory Algorithm for Outlier Detection—0
An Efficient Hashing-based Ensemble Method for Collaborative Outlier Detection—0
An Efficient Outlier Detection Algorithm for Data Streaming—0
An Empirical Exploration of Open-Set Recognition via Lightweight Statistical Pipelines—0
An Evaluation of Classification and Outlier Detection Algorithms—0
An Evolutionary Game based Secure Clustering Protocol with Fuzzy Trust Evaluation and Outlier Detection for Wireless Sensor Networks—0
Component-wise Adaptive Trimming For Robust Mixture Regression—0
A New Approach To Two-View Motion Segmentation Using Global Dimension Minimization—0
An Improved Heart Disease Prediction Using Stacked Ensemble Method—0
An Isolation Forest Learning Based Outlier Detection Approach for Effectively Classifying Cyber Anomalies—0
Annealed Denoising score matching: learning Energy based model in high-dimensional spaces—0
AnoMalNet: Outlier Detection based Malaria Cell Image Classification Method Leveraging Deep Autoencoder—0
Anomalous Sound Detection Based on Machine Activity Detection—0
Anomaly Detection based on Zero-Shot Outlier Synthesis and Hierarchical Feature Distillation—0
Anomaly Detection by Robust Statistics—0
Anomaly Detection for an E-commerce Pricing System—0
Anomaly Detection for Unmanned Aerial Vehicle Sensor Data Using a Stacked Recurrent Autoencoder Method with Dynamic Thresholding—0
Anomaly Detection using Capsule Networks for High-dimensional Datasets—0
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