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 226–250 of 703 papers

TitleStatusHype
Diffusion Nets—0
Clustering with Outlier Removal—0
Discovering outliers in the Mars Express thermal power consumption patterns—0
Distance approximation using Isolation Forests—0
Distance Based Pattern Driven Mining for Outlier Detection in High Dimensional Big Dataset—0
Distance for Functional Data Clustering Based on Smoothing Parameter Commutation—0
Distributional Gaussian Process Layers for Outlier Detection in Image Segmentation—0
Automatic Outlier Rectification via Optimal Transport—0
A New Approach To Two-View Motion Segmentation Using Global Dimension Minimization—0
An Improved Heart Disease Prediction Using Stacked Ensemble Method—0
DOC-NAD: A Hybrid Deep One-class Classifier for Network Anomaly Detection—0
Automatic Unsupervised Outlier Model Selection—0
Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditions—0
Anomaly Rule Detection in Sequence Data—0
DRGRADUATE: uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images—0
ECO-AMLP: A Decision Support System using an Enhanced Class Outlier with Automatic Multilayer Perceptron for Diabetes Prediction—0
Anomaly-Injected Deep Support Vector Data Description for Text Outlier Detection—0
ECORS: An Ensembled Clustering Approach to Eradicate The Local And Global Outlier In Collaborative Filtering Recommender System—0
Closed-Form, Provable, and Robust PCA via Leverage Statistics and Innovation Search—0
Backdoor Defense in Federated Learning Using Differential Testing and Outlier Detection—0
EDoG: Adversarial Edge Detection For Graph Neural Networks—0
Adaptive Outlier Detection for Power MOSFETs Based on Gaussian Process Regression—0
A boosted outlier detection method based on the spectrum of the Laplacian matrix of a graph—0
BAHP: Benchmark of Assessing Word Embeddings in Historical Portuguese—0
Explainable and Robust Millimeter Wave Beam Alignment for AI-Native 6G Networks—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