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 126–150 of 703 papers

TitleStatusHype
Benchmarking Unsupervised Outlier Detection with Realistic Synthetic Data—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
Contextual Outlier Interpretation—0
Data refinement for fully unsupervised visual inspection using pre-trained networks—0
A review on outlier/anomaly detection in time series data—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
A Review of Change of Variable Formulas for Generative Modeling—0
Are Out-of-Distribution Detection Methods Effective on Large-Scale Datasets?—0
A Model for Spatial Outlier Detection Based on Weighted Neighborhood Relationship—0
A Deep Learning Anomaly Detection Method in Textual Data—0
Are Outlier Detection Methods Resilient to Sampling?—0
A refined convergence analysis of pDCA_e with applications to simultaneous sparse recovery and outlier detection—0
A Meta-Learning Algorithm for Interrogative Agendas—0
A Rank-Based Similarity Metric for Word Embeddings—0
Achieving differential privacy for k-nearest neighbors based outlier detection by data partitioning—0
Conditional Testing based on Localized Conformal p-values—0
Conformal Prediction with Cellwise Outliers: A Detect-then-Impute Approach—0
Symbiotic Hybrid Neural Network Watchdog For Outlier Detection—0
A probabilistic view on Riemannian machine learning models for SPD matrices—0
ALTBI: Constructing Improved Outlier Detection Models via Optimization of Inlier-Memorization Effect—0
A Practical Algorithm for Distributed Clustering and Outlier Detection—0
Applications of Data Mining Techniques for Vehicular Ad hoc Networks—0
ALRe: Outlier Detection for Guided Refinement—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