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 501–525 of 703 papers

TitleStatusHype
ALRe: Outlier Detection for Guided Refinement—0
ALTBI: Constructing Improved Outlier Detection Models via Optimization of Inlier-Memorization Effect—0
A Meta-Learning Algorithm for Interrogative Agendas—0
A Model for Spatial Outlier Detection Based on Weighted Neighborhood Relationship—0
A multi-stream deep neural network with late fuzzy fusion for real-world anomaly detection—0
Analysis of Learning from Positive and Unlabeled Data—0
Analyzing categorical time series with the R package ctsfeatures—0
An Approximate Bayesian Long Short-Term Memory Algorithm for Outlier Detection—0
An Efficient Hashing-based Ensemble Method for Collaborative Outlier Detection—0
An Efficient Outlier Detection Algorithm for Data Streaming—0
An Empirical Exploration of Open-Set Recognition via Lightweight Statistical Pipelines—0
An Evaluation of Classification and Outlier Detection Algorithms—0
An Evolutionary Game based Secure Clustering Protocol with Fuzzy Trust Evaluation and Outlier Detection for Wireless Sensor Networks—0
Component-wise Adaptive Trimming For Robust Mixture Regression—0
A New Approach To Two-View Motion Segmentation Using Global Dimension Minimization—0
An Improved Heart Disease Prediction Using Stacked Ensemble Method—0
An Isolation Forest Learning Based Outlier Detection Approach for Effectively Classifying Cyber Anomalies—0
Annealed Denoising score matching: learning Energy based model in high-dimensional spaces—0
AnoMalNet: Outlier Detection based Malaria Cell Image Classification Method Leveraging Deep Autoencoder—0
Anomalous Sound Detection Based on Machine Activity Detection—0
Anomaly Detection based on Zero-Shot Outlier Synthesis and Hierarchical Feature Distillation—0
Anomaly Detection by Robust Statistics—0
Anomaly Detection for an E-commerce Pricing System—0
Anomaly Detection for Unmanned Aerial Vehicle Sensor Data Using a Stacked Recurrent Autoencoder Method with Dynamic Thresholding—0
Anomaly Detection using Capsule Networks for High-dimensional Datasets—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