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 251–275 of 703 papers

TitleStatusHype
Active Relation Discovery: Towards General and Label-aware Open Relation Extraction—0
Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditions—0
Robust outlier detection by de-biasing VAE likelihoods—0
Automatic Unsupervised Outlier Model Selection—0
DOC-NAD: A Hybrid Deep One-class Classifier for Network Anomaly Detection—0
An Improved Heart Disease Prediction Using Stacked Ensemble Method—0
Automatic Outlier Rectification via Optimal Transport—0
Distributional Gaussian Process Layers for Outlier Detection in Image Segmentation—0
Distance for Functional Data Clustering Based on Smoothing Parameter Commutation—0
Automatically Identifying Pseudepigraphic Texts—0
A New Approach To Two-View Motion Segmentation Using Global Dimension Minimization—0
A Framework for Clustering Uncertain Data—0
Distance Based Pattern Driven Mining for Outlier Detection in High Dimensional Big Dataset—0
Distance approximation using Isolation Forests—0
Discovering outliers in the Mars Express thermal power consumption patterns—0
Component-wise Adaptive Trimming For Robust Mixture Regression—0
Diffusion Nets—0
Differential Privacy for Anomaly Detection: Analyzing the Trade-off Between Privacy and Explainability—0
Automated detection of business-relevant outliers in e-commerce conversion rate—0
Differentially Private Analysis of Outliers—0
An Evolutionary Game based Secure Clustering Protocol with Fuzzy Trust Evaluation and Outlier Detection for Wireless Sensor Networks—0
A feature construction framework based on outlier detection and discriminative pattern mining—0
Active Relation Discovery: Towards General and Label-aware OpenRE—0
3D Scanning: A Comprehensive Survey—0
Detect Professional Malicious User with Metric Learning in Recommender Systems—0
Show:102550
← PrevPage 11 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