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 201–250 of 703 papers

TitleStatusHype
Deep Sequence Modeling for Anomalous ISP Traffic Prediction—0
Fin-Fed-OD: Federated Outlier Detection on Financial Tabular Data—0
AI-enabled Blockchain: An Outlier-aware Consensus Protocol for Blockchain-based IoT Networks—0
Cognitive Deep Machine Can Train Itself—0
Toward Scalable and Unified Example-based Explanation and Outlier Detection—0
DEK-Forecaster: A Novel Deep Learning Model Integrated with EMD-KNN for Traffic Prediction—0
Cluster Purging: Efficient Outlier Detection based on Rate-Distortion Theory—0
Dense outlier detection and open-set recognition based on training with noisy negative images—0
A Non-Parametric Control Chart For High Frequency Multivariate Data—0
Detecting abnormal events in video using Narrowed Normality Clusters—0
EVBattery: A Large-Scale Electric Vehicle Dataset for Battery Health and Capacity Estimation—0
Detecting outliers by clustering algorithms—0
Detecting Outliers in Data with Correlated Measures—0
Detecting Point Outliers Using Prune-based Outlier Factor (PLOF)—0
A boosted outlier detection method based on the spectrum of the Laplacian matrix of a graph—0
Detecting Unusual Input-Output Associations in Multivariate Conditional Data—0
Detection of Abnormal Input-Output Associations—0
A Unified Framework for Center-based Clustering of Distributed Data—0
Detection of Peculiar Word Sense by Distance Metric Learning with Labeled Examples—0
Detection of Thin Boundaries between Different Types of Anomalies in Outlier Detection using Enhanced Neural Networks—0
Detect Professional Malicious User with Metric Learning in Recommender Systems—0
Clustering with Outlier Removal—0
Anomaly Rule Detection in Sequence Data—0
Differentially Private Analysis of Outliers—0
Differential Privacy for Anomaly Detection: Analyzing the Trade-off Between Privacy and Explainability—0
Diffusion Nets—0
Component-wise Adaptive Trimming For Robust Mixture Regression—0
Discovering outliers in the Mars Express thermal power consumption patterns—0
A feature construction framework based on outlier detection and discriminative pattern mining—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
Anomaly-Injected Deep Support Vector Data Description for Text Outlier Detection—0
Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditions—0
Closed-Form, Provable, and Robust PCA via Leverage Statistics and Innovation Search—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
Adaptive Outlier Detection for Power MOSFETs Based on Gaussian Process Regression—0
ECORS: An Ensembled Clustering Approach to Eradicate The Local And Global Outlier In Collaborative Filtering Recommender System—0
Explainable and Robust Millimeter Wave Beam Alignment for AI-Native 6G Networks—0
Backdoor Defense in Federated Learning Using Differential Testing and Outlier Detection—0
EDoG: Adversarial Edge Detection For Graph Neural Networks—0
Enhancing Sentiment Analysis Results through Outlier Detection Optimization—0
Efficient Bregman Range Search—0
BAHP: Benchmark of Assessing Word Embeddings in Historical Portuguese—0
Enhancing Intrusion Detection In Internet Of Vehicles Through Federated Learning—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