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 276–300 of 703 papers

TitleStatusHype
Autoencoder Watchdog Outlier Detection for Classifiers—0
Detection of Thin Boundaries between Different Types of Anomalies in Outlier Detection using Enhanced Neural Networks—0
Detection of Peculiar Word Sense by Distance Metric Learning with Labeled Examples—0
An Evaluation of Classification and Outlier Detection Algorithms—0
Detection of Abnormal Input-Output Associations—0
A Unified Framework for Center-based Clustering of Distributed Data—0
Detecting Unusual Input-Output Associations in Multivariate Conditional Data—0
Detecting Surprising Situations in Event Data—0
Attack Strength vs. Detectability Dilemma in Adversarial Machine Learning—0
An Empirical Exploration of Open-Set Recognition via Lightweight Statistical Pipelines—0
Detecting Point Outliers Using Prune-based Outlier Factor (PLOF)—0
Detecting Outliers in Data with Correlated Measures—0
A system for exploring big data: an iterative k-means searchlight for outlier detection on open health data—0
Detecting outliers by clustering algorithms—0
EVBattery: A Large-Scale Electric Vehicle Dataset for Battery Health and Capacity Estimation—0
A Study of Deep Learning for Network Traffic Data Forecasting—0
An Efficient Outlier Detection Algorithm for Data Streaming—0
Detecting abnormal events in video using Narrowed Normality Clusters—0
Dense outlier detection and open-set recognition based on training with noisy negative images—0
A specifically designed machine learning algorithm for GNSS position time series prediction and its applications in outlier and anomaly detection and earthquake prediction—0
An Efficient Hashing-based Ensemble Method for Collaborative Outlier Detection—0
Active Learning of SVDD Hyperparameter Values—0
DEK-Forecaster: A Novel Deep Learning Model Integrated with EMD-KNN for Traffic Prediction—0
Toward Scalable and Unified Example-based Explanation and Outlier Detection—0
Deep Variational Semi-Supervised Novelty Detection—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