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 551–575 of 703 papers

TitleStatusHype
A specifically designed machine learning algorithm for GNSS position time series prediction and its applications in outlier and anomaly detection and earthquake prediction—0
A Study of Deep Learning for Network Traffic Data Forecasting—0
A system for exploring big data: an iterative k-means searchlight for outlier detection on open health data—0
Attack Strength vs. Detectability Dilemma in Adversarial Machine Learning—0
A Unified Framework for Center-based Clustering of Distributed Data—0
Autoencoder Watchdog Outlier Detection for Classifiers—0
Automated detection of business-relevant outliers in e-commerce conversion rate—0
Automatically Identifying Pseudepigraphic Texts—0
Automatic Outlier Rectification via Optimal Transport—0
Automatic Unsupervised Outlier Model Selection—0
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning—0
AWT -- Clustering Meteorological Time Series Using an Aggregated Wavelet Tree—0
Backdoor Defense in Federated Learning Using Differential Testing and Outlier Detection—0
Backdooring Outlier Detection Methods: A Novel Attack Approach—0
BAHP: Benchmark of Assessing Word Embeddings in Historical Portuguese—0
Benchmarking Unsupervised Outlier Detection with Realistic Synthetic Data—0
Blind Image Deblurring With Outlier Handling—0
Blockchain-Empowered Cyber-Secure Federated Learning for Trustworthy Edge Computing—0
BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learning—0
Boundary Peeling: Outlier Detection Method Using One-Class Peeling—0
Brain Ageing Prediction using Isolation Forest Technique and Residual Neural Network (ResNet)—0
Breakdown Point of Robust Support Vector Machine—0
Byzantine-Resilient Secure Federated Learning—0
ByzSecAgg: A Byzantine-Resistant Secure Aggregation Scheme for Federated Learning Based on Coded Computing and Vector Commitment—0
C-AllOut: Catching & Calling Outliers by Type—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